{
    "name": "advanced-dubbing-studio",
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "network-read",
                "filesystem-write"
            ],
            "requires_tools": [
                "dubbing_generate",
                "dubbing_status",
                "dubbing_download",
                "audio_provider_capabilities"
            ]
        }
    },
    "triggers": [
        "dubbing",
        "dub this",
        "多语言配音",
        "视频配音",
        "翻译配音",
        "译制"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Submit audio or video for multilingual dubbing, poll status, and download dubbed audio. Use when the user asks for dubbing, 多语言配音, 视频翻译配音, 译制片, or wants a source clip dubbed into another language."
}

{
    "name": "ai-video-script",
    "metadata": {
        "opensquilla": {
            "risk": "low",
            "capabilities": []
        }
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/aguo333\/ai-video-script",
        "maintained_by": "OpenSquilla",
        "upstream_version": "1.0.0"
    },
    "description": "Generate a structured short-video shooting script from a topic. Emits a strict, machine-parseable shot list (3 shots by default) with image prompt + video prompt + voiceover + on-screen text per shot. Trigger when the user asks for a video script, 分镜, 短视频文案, AI视频, 短剧脚本, or wants visual prompts ready for image\/video generation."
}

{
    "name": "audio-cog",
    "author": "CellCog",
    "homepage": "https:\/\/cellcog.ai",
    "metadata": {
        "openclaw": {
            "os": [
                "darwin",
                "linux",
                "windows"
            ],
            "emoji": "🎵",
            "requires": {
                "env": [
                    "CELLCOG_API_KEY"
                ],
                "bins": [
                    "python3"
                ]
            }
        },
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "env": [],
                "bins": [
                    "python3"
                ],
                "config": [
                    "awesome_webpage.provider",
                    "awesome_webpage.openrouter.api_key",
                    "awesome_webpage.openrouter.api_key_env",
                    "awesome_webpage.openrouter.models.audio_generation",
                    "awesome_webpage.output_dir"
                ]
            },
            "capabilities": [
                "network-write",
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "env": {
            "{{ with.api_key_env | default('OPENROUTER_API_KEY') }}": "{{ with.api_key | default('') }}"
        },
        "args": [
            "--model",
            "{{ with.model | default('openai\/gpt-audio-mini') }}",
            "--base-url",
            "{{ with.base_url | default('https:\/\/openrouter.ai\/api\/v1') }}",
            "--api-key-env",
            "{{ with.api_key_env | default('OPENROUTER_API_KEY') }}",
            "--output-dir",
            "{{ with.output_dir }}",
            "--filename",
            "{{ with.filename | default('narration.wav') }}",
            "--voice",
            "{{ with.voice | default('cedar') }}"
        ],
        "parse": "text",
        "stdin": "{{ with.payload | default(with.prompt | default(inputs.user_message)) }}",
        "command": "python {baseDir}\/scripts\/openrouter_audio.py",
        "timeout": 240
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/skills\/audio-cog",
        "maintained_by": "OpenSquilla"
    },
    "description": "OpenSquilla-compatible audio generation adapter for webpage audio requests. Prefer OpenRouter config\/API key in OpenSquilla; preserve the upstream CellCog workflow only as optional ClawHub provenance.",
    "dependencies": [
        "cellcog"
    ]
}

{
    "name": "awesome-webpage-image-download",
    "homepage": "",
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "requires": {
                "bins": [
                    "python3"
                ],
                "config": [
                    "awesome_webpage.output_dir"
                ]
            },
            "capabilities": [
                "network-read",
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--output-dir",
            "{{ with.output_dir }}",
            "--local-path-prefix",
            "{{ with.local_path_prefix | default('project\/assets\/images') }}"
        ],
        "parse": "text",
        "stdin": "{{ with.payload }}",
        "command": "python {baseDir}\/scripts\/image_download.py",
        "timeout": 120
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Deterministic image downloader for AwesomeWebpageMetaSkill search results. Use as skill_exec to fetch candidate image URLs into the configured local project tree without sandboxed shell curl.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "awesome-webpage-research",
    "homepage": "",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "env": [],
                "bins": []
            },
            "capabilities": [
                "network-read"
            ]
        }
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Single-pass mini research for AwesomeWebpageMetaSkill: produce a short cited topic brief from one bounded web-search round. Not a general deep-research replacement.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "kind": "meta",
    "name": "AwesomeWebpageMetaSkill",
    "always": false,
    "config": {
        "awesome_webpage": {
            "provider": "openrouter",
            "openrouter": {
                "models": {
                    "page_generation": "moonshotai\/kimi-k2.6",
                    "audio_generation": "openai\/gpt-audio-mini",
                    "image_generation": "google\/gemini-3-pro-image-preview",
                    "video_generation": "bytedance\/seedance-2.0-fast"
                },
                "api_key": null,
                "base_url": "https:\/\/openrouter.ai\/api\/v1",
                "api_key_env": "OPENROUTER_API_KEY"
            },
            "output_dir": "{{ inputs.workspace_dir }}\/awesome-webpage-output",
            "clawhub_skills": {
                "filesystem": {
                    "url": "https:\/\/clawhub.ai\/gtrusler\/clawdbot-filesystem",
                    "skill": "filesystem"
                },
                "web_search": {
                    "url": "https:\/\/clawhub.ai\/billyutw\/web-search",
                    "skill": "web-search"
                },
                "audio_generation": {
                    "url": "https:\/\/clawhub.ai\/skills\/audio-cog",
                    "skill": "audio-cog",
                    "opensquilla_compatibility": "openrouter-config-first"
                },
                "image_generation": {
                    "url": "https:\/\/clawhub.ai\/skills\/nano-banana-pro-openrouter",
                    "notes": "The image_aigc step runs this deterministic skill_exec adapter.\nIt does not invoke a prompt-building skill as an agent. No\nGEMINI_API_KEY required; only OPENROUTER_API_KEY.\n",
                    "skill": "nano-banana-pro-openrouter",
                    "opensquilla_compatibility": "deterministic-skill-exec"
                },
                "video_generation": {
                    "url": "openrouter-configured-video-generation",
                    "skill": "openrouter-video-generator"
                },
                "webpage_generation": {
                    "url": "https:\/\/clawhub.ai\/jhauga\/html-coder",
                    "notes": "Invoked by the webpage_generation step as the HTML\/CSS\/JS authoring\nskill. The meta-skill task constrains it to source JSON only;\npackaging, validation, repair, and media generation remain separate\nsteps.\n",
                    "skill": "html-coder",
                    "opensquilla_compatibility": "scoped-agent"
                }
            },
            "media_strategy": {
                "licensing": "prefer_reusable_or_user_supplied",
                "aigc_policy": "search_images_direct_generate_audio_video",
                "search_first": true,
                "target_assets": {
                    "audio": 1,
                    "video": 1,
                    "images": 6
                },
                "search_modalities": [
                    "images"
                ],
                "confirmation_steps": [
                    "ask_images",
                    "ask_audio",
                    "ask_video",
                    "ask_style"
                ],
                "default_modalities": [
                    "text",
                    "images",
                    "audio",
                    "video"
                ],
                "placeholder_policy": "visible_replacement_slot_when_generation_unavailable",
                "allow_remote_embeds": false,
                "direct_aigc_modalities": [
                    "audio",
                    "video"
                ],
                "aigc_fallback_when_search_empty": true
            }
        }
    },
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "requires": {
                "config": [
                    "awesome_webpage.provider",
                    "awesome_webpage.openrouter.api_key",
                    "awesome_webpage.openrouter.models.page_generation",
                    "awesome_webpage.openrouter.models.image_generation",
                    "awesome_webpage.openrouter.models.audio_generation",
                    "awesome_webpage.openrouter.models.video_generation",
                    "awesome_webpage.output_dir",
                    "awesome_webpage.media_strategy"
                ]
            },
            "capabilities": [
                "network-read",
                "network-write",
                "filesystem-read",
                "filesystem-write",
                "command-exec"
            ]
        }
    },
    "triggers": [
        "create awesome webpage",
        "build multimedia webpage project",
        "生成图文音视频网页",
        "做一个多媒体网页项目",
        "AwesomeWebpageMetaSkill"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "composition": {
        "steps": [
            {
                "id": "requirement_framing",
                "kind": "llm_chat",
                "with": {
                    "task": "{{ inputs.language_instruction }}\n\nExtract and normalize the user's webpage request.\n\nRequired fields:\n- topic\n- target_audience\n- output_language\n- visual_style\n- media_preferences: infer the user's requested images, audio, video, remote embeds, and local-only needs\n- configured_output_dir: use the resolved config below; only mark CONFIG_NEEDED if it is empty\n- constraints and risks\n\nDo not invent provider names, model ids, API keys, output paths, or media strategy values.\nThey must come from the `awesome_webpage` config.\nA missing OpenRouter model value means CONFIG_NEEDED for that generator; it does not mean\nthe user does not want that media modality. Do not mark audio or video as unwanted only\nbecause its configured model is null.\n\nResolved awesome_webpage config for this run:\nprovider: openrouter\nopenrouter.api_key_env: OPENROUTER_API_KEY\nopenrouter.models.page_generation: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('page_generation', 'moonshotai\/kimi-k2.6') }}\nopenrouter.models.image_generation: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('image_generation', 'google\/gemini-3-pro-image-preview') }}\nopenrouter.models.audio_generation: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('audio_generation', 'openai\/gpt-audio-mini') }}\nopenrouter.models.video_generation: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('video_generation', 'bytedance\/seedance-2.0-fast') }}\noutput_dir: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\nThis resolved output_dir is configured and must not be reported as CONFIG_NEEDED.\nmedia_strategy.search_first: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('search_first', true) }}\nmedia_strategy.search_modalities: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('search_modalities', ['images']) | join(', ') }}\nmedia_strategy.direct_aigc_modalities: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('direct_aigc_modalities', ['audio', 'video']) | join(', ') }}\nmedia_strategy.aigc_fallback_when_search_empty: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('aigc_fallback_when_search_empty', true) }}\nmedia_strategy.allow_remote_embeds: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('allow_remote_embeds', false) }}\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1600) }}\n",
                    "system": "You are the Requirement Framing stage for AwesomeWebpageMetaSkill.\nProduce a compact, structured brief. Do not call tools.\n"
                }
            },
            {
                "id": "project_slug",
                "kind": "llm_chat",
                "with": {
                    "task": "Output ONLY a single slug derived from the topic in the framed\nrequirements below. Constraints:\n- lowercase ASCII letters, digits, and hyphens only\n- no spaces, no path separators, no file extension, no quotes\n- max 40 characters\n- topic-derived, so different topics produce different slugs\n\nExamples (do NOT reuse — derive from the actual topic):\n- \"海洋塑料污染\" → ocean-plastic-pollution\n- \"Quantum computing intro\" → quantum-computing-intro\n- \"我们公司年会回顾\" → company-annual-recap\n\nIf you cannot derive a meaningful slug, output `webpage`.\n\nOutput: a single line containing just the slug. No prefix, no\nexplanation, no backticks, no quotes.\n\nFramed requirements:\n{{ outputs.requirement_framing | truncate(1500) }}\n",
                    "system": "You produce a single filesystem-safe slug identifying this webpage\nproject. Do not call tools.\n"
                },
                "depends_on": [
                    "requirement_framing"
                ]
            },
            {
                "id": "ask_images",
                "kind": "user_input",
                "clarify": {
                    "mode": "chat",
                    "intro": "先逐项确认网页媒体配置，再继续研究、搜索素材和生成项目。\n默认需要图片；后续会先搜索，搜不到合适素材才生成。\n",
                    "fields": [
                        {
                            "name": "include_images",
                            "type": "enum",
                            "prompt": "是否需要图片？",
                            "choices": [
                                "YES",
                                "NO"
                            ],
                            "default": "YES",
                            "prompt_en": "Do you want images?",
                            "prompt_zh": "是否需要图片？"
                        }
                    ],
                    "intro_en": "I will confirm the webpage media settings before research, asset search,\nand project generation. Images are included by default; I will search\nfirst and generate only when suitable assets are unavailable.\n",
                    "intro_zh": "先逐项确认网页媒体配置，再继续研究、搜索素材和生成项目。\n默认需要图片；后续会先搜索，搜不到合适素材才生成。\n",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "取消",
                        "算了",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "requirement_framing"
                ]
            },
            {
                "id": "ask_audio",
                "kind": "user_input",
                "clarify": {
                    "mode": "chat",
                    "intro": "已记录图片选择。现在确认音频。\n默认需要音频；音频不走素材搜索，会直接按 OpenRouter 配置生成或给出可替换位置。\n",
                    "fields": [
                        {
                            "name": "include_audio",
                            "type": "enum",
                            "prompt": "是否需要音频？",
                            "choices": [
                                "YES",
                                "NO"
                            ],
                            "default": "YES",
                            "prompt_en": "Do you want audio?",
                            "prompt_zh": "是否需要音频？"
                        }
                    ],
                    "intro_en": "I recorded the image choice. Now I need to confirm audio.\nAudio is included by default; it is generated from the OpenRouter\nconfiguration or represented as a replaceable slot.\n",
                    "intro_zh": "已记录图片选择。现在确认音频。\n默认需要音频；音频不走素材搜索，会直接按 OpenRouter 配置生成或给出可替换位置。\n",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "取消",
                        "算了",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "ask_images"
                ]
            },
            {
                "id": "ask_video",
                "kind": "user_input",
                "clarify": {
                    "mode": "chat",
                    "intro": "已记录音频选择。现在确认视频。\n默认需要视频；视频不走素材搜索，会直接按 OpenRouter 配置生成或给出可替换位置。\n",
                    "fields": [
                        {
                            "name": "include_video",
                            "type": "enum",
                            "prompt": "是否需要视频？",
                            "choices": [
                                "YES",
                                "NO"
                            ],
                            "default": "YES",
                            "prompt_en": "Do you want video?",
                            "prompt_zh": "是否需要视频？"
                        }
                    ],
                    "intro_en": "I recorded the audio choice. Now I need to confirm video.\nVideo is included by default; it is generated from the OpenRouter\nconfiguration or represented as a replaceable slot.\n",
                    "intro_zh": "已记录音频选择。现在确认视频。\n默认需要视频；视频不走素材搜索，会直接按 OpenRouter 配置生成或给出可替换位置。\n",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "取消",
                        "算了",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "ask_audio"
                ]
            },
            {
                "id": "ask_style",
                "kind": "user_input",
                "clarify": {
                    "mode": "chat",
                    "intro": "已记录视频选择。最后确认网页整体风格，然后开始研究、规划和素材获取。\n",
                    "fields": [
                        {
                            "name": "visual_style",
                            "type": "string",
                            "prompt": "网页整体风格是什么？例如：科技感、纪录片风、极简、儿童科普、商业发布会风。",
                            "max_chars": 500,
                            "prompt_en": "What overall style should the webpage use? For example: futuristic, documentary, minimal, kids science, or product launch.",
                            "prompt_zh": "网页整体风格是什么？例如：科技感、纪录片风、极简、儿童科普、商业发布会风。"
                        }
                    ],
                    "intro_en": "I recorded the video choice. Last, confirm the overall visual style;\nthen I will start research, planning, and asset collection.\n",
                    "intro_zh": "已记录视频选择。最后确认网页整体风格，然后开始研究、规划和素材获取。\n",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "取消",
                        "算了",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "ask_video"
                ]
            },
            {
                "id": "deep_research",
                "kind": "agent",
                "with": {
                    "question": "Research the topic for a multimedia webpage project.\n\nSingle bounded round only. Produce a short cited brief with 3-5\npage anchors. Do not run multi-round investigation.\n\nRequirement framing:\n{{ outputs.requirement_framing | truncate(3000) }}\n\nConfirmed interactive choices:\nimages: {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}\naudio: {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n\nOriginal request:\n{{ inputs.user_message | xml_escape | truncate(1000) }}\n"
                },
                "skill": "awesome-webpage-research",
                "depends_on": [
                    "requirement_framing",
                    "ask_images",
                    "ask_audio",
                    "ask_video",
                    "ask_style"
                ]
            },
            {
                "id": "page_outline",
                "kind": "llm_chat",
                "with": {
                    "task": "{{ inputs.language_instruction }}\n\nOutput the webpage OUTLINE — section structure + media intents\nonly. Downstream media steps read this to decide WHAT to fetch\nor generate; the deterministic `media_assets_collect` step later\nturns producer output into concrete browser-relative asset paths.\n\nRequired sections:\n\n1. Page hierarchy\n   - Ordered list of sections: section_id, title, narrative\n     purpose, 1-2 sentence summary.\n   - Overall narrative arc (one paragraph).\n\n2. Visual style summary (one paragraph)\n\n3. Media intent list — one entry per desired asset, with:\n   - slot_id: short kebab-case key, unique within outline\n     (e.g. `hero-ocean`, `foodchain-flow`, `narration-intro`).\n   - modality: image | audio | video\n   - placement: which section_id it belongs to + role\n     (e.g. \"hero background\", \"section illustration\",\n     \"section narration\").\n   - subject: one-sentence concrete description of what the\n     asset should depict \/ convey.\n   - prompt_hint: 1-2 sentences of stylistic \/ compositional\n     guidance for AIGC steps.\n   - search_keywords: 3-6 short keywords for image search\n     (only meaningful for image slots).\n   - load_bearing: true | false. `true` means the section\n     structurally relies on this asset (e.g. hero video). If\n     the asset later fails to produce, the final media binding\n     gate reports it instead of hiding the missing modality.\n     `false` means the section degrades gracefully to\n     text-only.\n\n   HARD: do NOT specify filenames or paths. slot_id is a\n   semantic key; the producer step (image_download,\n   image_aigc, audio_aigc, video_aigc) decides the on-disk\n   filename and the deterministic media binding step performs\n   final src\/path checks.\n\n4. Local file plan: project\/index.html, project\/style.css,\n   project\/script.js.\n\n5. Accessibility plan: alt-text strategy, ARIA roles,\n   keyboard navigation, reduced-motion handling.\n\nHard rules:\n- No filenames. No paths. No `.jpg` \/ `.mp3` \/ `.mp4`.\n- If the user said NO to a modality, do not include any media\n  intents of that modality.\n- Include only as many media intents as the section narrative\n  actually needs — do not pad to hit a target count.\n- Outline must remain coherent even if every media intent\n  fails downstream (text + headings carry the page).\n\nRequirement framing:\n{{ outputs.requirement_framing | truncate(3000) }}\n\nResearch report:\n{{ outputs.deep_research | truncate(6000) }}\n\nConfirmed interactive choices:\nimages: {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}\naudio: {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n",
                    "system": "You are the Page Outline stage for AwesomeWebpageMetaSkill.\nProduce a structural outline + media intent list — NOT a final\nasset binding. Later steps will normalize media results into a\ncompact manifest and then bind actual files to sections. Do not\ncall tools.\n"
                },
                "depends_on": [
                    "requirement_framing",
                    "project_slug",
                    "ask_images",
                    "ask_audio",
                    "ask_video",
                    "ask_style",
                    "deep_research"
                ]
            },
            {
                "id": "media_slots_normalize",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "stdin": "{\"page_outline\": {{ outputs.page_outline | tojson }},\n \"requirement_framing\": {{ outputs.requirement_framing | tojson }},\n \"include_image\": {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') | tojson }},\n \"include_audio\": {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') | tojson }},\n \"include_video\": {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') | tojson }},\n \"visual_style\": {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') | tojson }}}\n",
                    "command": "python -m opensquilla.skills.bundled.AwesomeWebpageMetaSkill.scripts.media_slots_normalize\n",
                    "timeout": 20
                },
                "depends_on": [
                    "page_outline"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "media_search",
                "kind": "agent",
                "when": "inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') == 'YES' and inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('search_first', True) and 'images' in inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('search_modalities', ['images'])",
                "with": {
                    "query": "Search only for reusable webpage image candidates for this plan.\nDo not search for audio or video; those modalities are generated directly\nthrough the configured OpenRouter models when the user requests them.\nPrefer assets that can be used locally or replaced cleanly. Return URLs, titles,\nimage type, likely license\/provenance, and download\/replacement notes.\n\nTool contract for this step:\n- Use the platform-provided `web_search` tool ONLY (provider is configured\n  globally; you do not need to know which engine — just call the tool).\n- Do NOT run any local Python script (no `python scripts\/search.py`,\n  no `duckduckgo-search`, no `pip install`).\n- Do NOT call `web_fetch`. Snippets from `web_search` are enough.\n- Issue at most 2 `web_search` calls, each using only the supported\n  arguments `query` and `max_results=6`; include image intent in the\n  query text itself, grouped by visual theme — do not search per asset.\n- Stop as soon as you have ~4-6 usable candidates total.\n- If results are empty, unusable, off-topic, or licensing is unclear,\n  stop immediately and return:\n  {\"status\":\"NO_USABLE_IMAGE_CANDIDATES\",\"candidates\":[]}\n- Otherwise return one JSON object with:\n  candidates[] (title, url, source_domain, likely_license, suggested_alt).\n\nNormalized media slots (authoritative):\n{{ outputs.media_slots_normalize | truncate(3500) }}\n\nPage plan context:\n{{ outputs.page_outline | truncate(1200) }}\n\nConfirmed interactive choices:\nimages: {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}\naudio: {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n",
                    "format": "json"
                },
                "skill": "web-search",
                "depends_on": [
                    "media_slots_normalize"
                ]
            },
            {
                "id": "media_strategy",
                "kind": "llm_classify",
                "with": {
                    "text": "Decide whether searched image media is enough or whether image AIGC fallback is required.\n\nFast media policy:\n- Use the confirmed interactive choices as the source of truth.\n- Search is image-only and only runs when config.awesome_webpage.media_strategy.search_first\n  is true and `images` is present in search_modalities.\n- When image search is disabled by config, ignore media_search output and choose\n  NEEDS_AIGC_IMAGE for requested images so the direct image generator handles them.\n- Audio and video must not be evaluated through web search.\n- If include_images is NO, choose IMAGE_SEARCH_READY.\n- If include_images is YES, evaluate only image candidates from web-search.\n- Choose NEEDS_AIGC_IMAGE when image search is empty, unusable, off-topic,\n  not downloadable\/localizable, or has unclear licensing.\n- Choose IMAGE_SEARCH_READY only when the search results provide enough usable\n  local\/downloadable image assets for the requested page.\n- Audio and video are direct AIGC modalities. They are handled by audio_aigc\n  and video_aigc based on user confirmation and OpenRouter config, not by this classifier.\n\nRules:\n- IMAGE_SEARCH_READY: image search results are sufficient, or images were not requested.\n- NEEDS_AIGC_IMAGE: image generation is needed.\n\nIf search is empty and config.awesome_webpage.media_strategy.aigc_fallback_when_search_empty is true,\nchoose NEEDS_AIGC_IMAGE. Do not choose a model here; model choice belongs to config.\n\nResolved media strategy:\nsearch_first: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('search_first', true) }}\nsearch_modalities: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('search_modalities', ['images']) | join(', ') }}\naigc_fallback_when_search_empty: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('aigc_fallback_when_search_empty', true) }}\n\nRequirement framing:\n{{ outputs.requirement_framing | truncate(2000) }}\n\nPage plan:\n{{ outputs.page_outline | truncate(2500) }}\n\nNormalized media slots:\n{{ outputs.media_slots_normalize | truncate(2000) }}\n\nConfirmed interactive choices:\nimages: {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}\naudio: {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n\nPrimary search:\n{{ outputs.media_search | truncate(3500) }}\n"
                },
                "depends_on": [
                    "media_search",
                    "media_slots_normalize"
                ],
                "output_choices": [
                    "IMAGE_SEARCH_READY",
                    "NEEDS_AIGC_IMAGE"
                ]
            },
            {
                "id": "image_download",
                "kind": "skill_exec",
                "when": "inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') == 'YES' and outputs.media_strategy == 'IMAGE_SEARCH_READY'",
                "with": {
                    "payload": "{\"media_slots\": {{ outputs.media_slots_normalize | tojson }},\n \"media_search\": {{ outputs.media_search | tojson }}}\n",
                    "output_dir": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\/project\/assets\/images",
                    "local_path_prefix": "project\/assets\/images"
                },
                "skill": "awesome-webpage-image-download",
                "depends_on": [
                    "media_strategy",
                    "media_slots_normalize"
                ]
            },
            {
                "id": "image_aigc",
                "kind": "skill_exec",
                "when": "inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') == 'YES' and (outputs.media_strategy == 'NEEDS_AIGC_IMAGE' or 'IMAGE_DOWNLOAD_INCOMPLETE:' in outputs.get('image_download', '') or (outputs.media_strategy == 'IMAGE_SEARCH_READY' and 'IMAGE_READY:' not in outputs.get('image_download', '')))",
                "with": {
                    "model": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('image_generation', 'google\/gemini-3-pro-image-preview') }}",
                    "api_key": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('api_key', '') }}",
                    "payload": "{\n  \"requirement_framing\": {{ outputs.requirement_framing | tojson }},\n  \"media_slots\": {{ outputs.media_slots_normalize | tojson }},\n  \"page_outline\": {{ outputs.page_outline | tojson }},\n  \"image_download\": {{ outputs.get('image_download', '') | tojson }},\n  \"include_images\": {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') | tojson }},\n  \"visual_style\": {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') | tojson }}\n}\n",
                    "base_url": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('base_url', 'https:\/\/openrouter.ai\/api\/v1') }}",
                    "filename": "{{ outputs.project_slug | slugify }}.png",
                    "max_images": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('media_strategy', {}).get('target_assets', {}).get('images', 6) }}",
                    "output_dir": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\/project\/assets\/images",
                    "resolution": "1K",
                    "api_key_env": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('api_key_env', 'OPENROUTER_API_KEY') }}",
                    "local_path_prefix": "project\/assets\/images"
                },
                "skill": "nano-banana-pro-openrouter",
                "depends_on": [
                    "media_strategy",
                    "image_download",
                    "media_slots_normalize"
                ]
            },
            {
                "id": "audio_aigc",
                "kind": "skill_exec",
                "when": "inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') == 'YES'",
                "with": {
                    "model": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('audio_generation', 'openai\/gpt-audio-mini') }}",
                    "voice": "cedar",
                    "api_key": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('api_key', '') }}",
                    "payload": "{\n  \"script\": {{ outputs.audio_script | tojson }},\n  \"requirement_framing\": {{ outputs.requirement_framing | tojson }},\n  \"media_slots\": {{ outputs.media_slots_normalize | tojson }}\n}\n",
                    "base_url": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('base_url', 'https:\/\/openrouter.ai\/api\/v1') }}",
                    "filename": "{{ outputs.project_slug | slugify }}-narration.wav",
                    "output_dir": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\/project\/assets\/audio",
                    "api_key_env": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('api_key_env', 'OPENROUTER_API_KEY') }}"
                },
                "skill": "audio-cog",
                "depends_on": [
                    "audio_script"
                ]
            },
            {
                "id": "audio_script",
                "kind": "llm_chat",
                "when": "inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') == 'YES'",
                "with": {
                    "task": "{{ inputs.language_instruction }}\n\nWrite the exact narration script that the audio model should speak.\nOutput spoken text only. No title, no markdown, no filename, no JSON,\nno labels, no stage directions, no \"我明白了\", no \"接下来\", no\n\"我将为你生成\", no assistant-style acknowledgement.\n\nRequirements:\n- Make it a polished webpage narration or guide, not a response to\n  the user.\n- 45-75 seconds when spoken.\n- Match the requested language and style.\n- Use the audio slot placement\/subject from media slots when present.\n- It must stand alone as content a visitor hears on the page.\n\nRequirement framing:\n{{ outputs.requirement_framing | truncate(1800) }}\n\nPage outline:\n{{ outputs.page_outline | truncate(2600) }}\n\nNormalized media slots:\n{{ outputs.media_slots_normalize | truncate(2200) }}\n",
                    "system": "You write only final spoken narration text for webpage audio.\nDo not call tools. Do not acknowledge the request.\n"
                },
                "depends_on": [
                    "requirement_framing",
                    "page_outline",
                    "media_slots_normalize"
                ]
            },
            {
                "id": "video_aigc",
                "kind": "skill_exec",
                "when": "inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') == 'YES'",
                "with": {
                    "model": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('models', {}).get('video_generation', 'bytedance\/seedance-2.0-fast') }}",
                    "prompt": "Generate the short video asset requested by the page outline. Do not\nrun web search for video assets.\n\nRequirement framing:\n{{ outputs.requirement_framing | truncate(1800) }}\n\nPage plan:\n{{ outputs.page_outline | truncate(3000) }}\n\nConfirmed interactive choices:\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n",
                    "api_key": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('api_key', '') }}",
                    "base_url": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('base_url', 'https:\/\/openrouter.ai\/api\/v1') }}",
                    "duration": "10",
                    "filename": "{{ outputs.project_slug | slugify }}-intro.mp4",
                    "output_dir": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\/project\/assets\/video",
                    "api_key_env": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('openrouter', {}).get('api_key_env', 'OPENROUTER_API_KEY') }}",
                    "aspect_ratio": "16:9"
                },
                "skill": "openrouter-video-generator",
                "depends_on": [
                    "page_outline"
                ]
            },
            {
                "id": "media_assets_collect",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "env": {
                        "AUDIO_AIGC": "{{ outputs.get('audio_aigc', '') }}",
                        "IMAGE_AIGC": "{{ outputs.get('image_aigc', '') }}",
                        "VIDEO_AIGC": "{{ outputs.get('video_aigc', '') }}",
                        "PROJECT_ROOT": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\/project",
                        "IMAGE_DOWNLOAD": "{{ outputs.get('image_download', '') }}"
                    },
                    "command": "python -m opensquilla.skills.bundled.AwesomeWebpageMetaSkill.scripts.media_assets_collect\n",
                    "timeout": 30
                },
                "depends_on": [
                    "image_download",
                    "image_aigc",
                    "audio_aigc",
                    "video_aigc"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "webpage_generation",
                "kind": "agent",
                "with": {
                    "mode": "generate",
                    "task": "{{ inputs.language_instruction }}\n\nYou are the Webpage Source Authoring stage for AwesomeWebpageMetaSkill.\nProduce source text only. Do not call tools and do not write files.\nApply a professional HTML\/CSS quality standard: clear visual\nhierarchy, semantic HTML, restrained but distinctive typography and\ncolor, accessible media controls, responsive layout, and intentional\ncomposition rather than a thin placeholder page.\n\nGenerate a complete local webpage as strict JSON with exactly these keys:\n- `index_html`\n- `style_css`\n- `script_js`\n- `summary`\n\nOutput JSON only. Do not wrap it in Markdown fences. Ignore\nhtml-coder's default Markdown\/code-block output format for this\ninvocation; the final answer must be the strict JSON object only.\n\nScope:\n- Author only the contents for project\/index.html, project\/style.css,\n  and project\/script.js.\n- Do not download, search, generate, copy, move, delete, package, validate, repair, normalize paths, or clean up assets.\n- Do not mention or choose filesystem paths. A deterministic later\n  step writes the files to the exact project root.\n- Use only browser-relative `assets\/...` paths listed in\n  `media_assets_collect.assets[]`.\n- Include available image, audio, and video assets in the authored\n  page. A deterministic later step patches any asset the model misses.\n- Do not show \"pending\", \"to be added\", or \"replace this asset\"\n  placeholders for requested media.\n- Place audio controls according to the audio slot placement in\n  `media_slots_normalize.slots[]`; for narration\/guide audio, put\n  the player near the intro\/hero or the referenced section, never as\n  footer-only\/end-of-page content unless the slot explicitly says so.\n\nDesign-quality contract, adapted from html-coder\n(https:\/\/clawhub.ai\/jhauga\/html-coder):\n- Build a real, production-quality webpage, not a short landing-page\n  stub. Represent the page outline in semantic\n  HTML (`header`, `main`, `section`, `figure`, `footer`).\n- Use strong visual hierarchy, clear grid alignment, intentional\n  whitespace, readable line lengths, and a balanced 2-3 color system.\n  Avoid a one-note dark\/slate\/blue page unless the requested style\n  specifically requires it.\n- Include accessible local media: `<audio controls>` for any\n  audio asset, `<video>` for any video asset, and `<img>` with\n  meaningful alt text\/captions for every image asset.\n- If `media_assets_collect` contains extra image assets, render a gallery,\n  evidence wall, or visual sequence section so generated assets do\n  not sit unused on disk.\n- Add keyboard-accessible controls where interaction exists, visible\n  focus states, reduced-motion handling, and mobile layouts without\n  overlapping text.\n- Do not show \"replace this asset\" placeholders when a real asset is\n  present in `media_assets_collect`.\n\nMedia assets:\nThe authoritative asset facts are `media_assets_collect` below.\nUse only `media_assets_collect.assets[].src` values in generated\nHTML\/CSS\/JS. Those values are already `assets\/...` browser paths.\nDo not scan raw producer output and do not use raw\n`project\/assets\/...` local_path values as browser src paths.\n\nPage outline (narrative + section structure):\n{{ outputs.page_outline | truncate(1500) }}\n\nNormalized media slots (placement\/role contract):\n{{ outputs.media_slots_normalize | truncate(3500) }}\n\nMedia strategy:\n{{ outputs.media_strategy }}\n\nConfirmed interactive choices:\nimages: {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}\naudio: {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n\nMedia assets:\n{{ outputs.media_assets_collect | truncate(6000) }}\n"
                },
                "skill": "html-coder",
                "depends_on": [
                    "requirement_framing",
                    "deep_research",
                    "page_outline",
                    "media_slots_normalize",
                    "media_assets_collect"
                ]
            },
            {
                "id": "webpage_source_validate",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "stdin": "{{ outputs.webpage_generation | tojson }}",
                    "command": "python -m opensquilla.skills.bundled.AwesomeWebpageMetaSkill.scripts.webpage_source_validate\n",
                    "timeout": 15
                },
                "depends_on": [
                    "webpage_generation"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "webpage_generation_retry",
                "kind": "llm_chat",
                "when": "not outputs.get('webpage_generation', '').strip() or 'WEBPAGE_SOURCE_INVALID:' in outputs.get('webpage_source_validate', '')",
                "with": {
                    "task": "{{ inputs.language_instruction }}\n\nGenerate strict JSON with exactly these keys:\n- `index_html`\n- `style_css`\n- `script_js`\n- `summary`\n\nOutput JSON only. Do not wrap it in Markdown fences.\n\nScope:\n- Author only project\/index.html, project\/style.css, and\n  project\/script.js contents.\n- Do not call tools, write files, download assets, search, generate\n  media, package, validate, repair, or normalize paths.\n- Use only `assets\/...` browser paths already present in\n  media_assets_collect.assets[].src.\n- Do not invent pending audio\/video controls or \"to be added\"\n  placeholders.\n- Place audio controls near the audio slot placement from\n  media_slots_normalize, not as footer-only\/end-of-page content\n  unless the slot explicitly says footer.\n- Keep the page production-quality: semantic sections, accessible\n  local media, responsive layout, meaningful hierarchy, and no\n  overlapping mobile text.\n\nPage outline:\n{{ outputs.page_outline | truncate(900) }}\n\nNormalized media slots:\n{{ outputs.media_slots_normalize | truncate(2200) }}\n\nMedia assets:\n{{ outputs.media_assets_collect | truncate(3500) }}\n\nConfirmed interactive choices:\nimages: {{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}\naudio: {{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}\nvideo: {{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}\nvisual_style: {{ inputs.get('collected', {}).get('ask_style', {}).get('visual_style', '') }}\n",
                    "system": "You are the fallback Webpage Source Authoring stage for\nAwesomeWebpageMetaSkill. The primary source authoring step returned\nempty or invalid output. Produce compact source JSON only.\n"
                },
                "depends_on": [
                    "webpage_generation",
                    "webpage_source_validate",
                    "media_slots_normalize"
                ]
            },
            {
                "id": "webpage_write",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "env": {
                        "PROJECT_ROOT": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}",
                        "WORKSPACE_DIR": "{{ inputs.workspace_dir }}"
                    },
                    "stdin": "{{ (outputs.get('webpage_generation_retry', '') or outputs.webpage_generation) | tojson }}",
                    "command": "python -m opensquilla.skills.bundled.AwesomeWebpageMetaSkill.scripts.webpage_write\n",
                    "timeout": 30
                },
                "depends_on": [
                    "webpage_generation",
                    "webpage_source_validate",
                    "webpage_generation_retry"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "media_bind_validate",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "env": {
                        "AUDIO_AIGC": "{{ outputs.get('audio_aigc', '') }}",
                        "IMAGE_AIGC": "{{ outputs.get('image_aigc', '') }}",
                        "VIDEO_AIGC": "{{ outputs.get('video_aigc', '') }}",
                        "PROJECT_ROOT": "{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}",
                        "INCLUDE_AUDIO": "{{ inputs.get('collected', {}).get('ask_audio', {}).get('include_audio', 'YES') }}",
                        "INCLUDE_IMAGE": "{{ inputs.get('collected', {}).get('ask_images', {}).get('include_images', 'YES') }}",
                        "INCLUDE_VIDEO": "{{ inputs.get('collected', {}).get('ask_video', {}).get('include_video', 'YES') }}",
                        "IMAGE_DOWNLOAD": "{{ outputs.get('image_download', '') }}"
                    },
                    "command": "python -m opensquilla.skills.bundled.AwesomeWebpageMetaSkill.scripts.media_bind_validate\n",
                    "timeout": 30
                },
                "depends_on": [
                    "webpage_write",
                    "media_assets_collect"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "quick_validate",
                "kind": "agent",
                "with": {
                    "task": "Quick path-level sanity pass over the generated local webpage project.\n\nFixed ClawHub skill URL: https:\/\/clawhub.ai\/gtrusler\/clawdbot-filesystem\nUse only the per-project root:\n{{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\nDo not choose a new output directory and do not install another filesystem skill.\n\nExpected tree:\n- project\/index.html\n- project\/style.css\n- project\/script.js\n- project\/assets\/images\n- project\/assets\/audio\n- project\/assets\/video\n\nAllowed operations (path-level only, no content reads):\n- `test -f` each of the three authored files; report MISSING_FILE\n  with the specific path for any missing one.\n- `ls` the three asset directories; missing dirs are warnings, not\n  failures. `mkdir -p` to create any missing asset directory.\n- Normalize relative links at the path level only — do not open\n  files to check link targets.\n\nHard prohibitions:\n- Do NOT run `cat`, `head`, `tail`, `sed`, `grep`, `wc`, `diff`,\n  or any content-inspection command on index.html, style.css, or\n  script.js. Trust the webpage_generation output as the source of\n  truth.\n- Do not regenerate, rewrite, or patch authored file content. If a\n  file is missing, report it; do not synthesize a replacement.\n- Skip any packaging step (zip\/tar) — the project tree is the\n  deliverable.\n\nOutput: a 1-paragraph summary stating either VALIDATED (all three\nauthored files exist and asset directories are present) or one of\nMISSING_FILE \/ MISSING_ASSETS with the specific path. Stop within\nthree shell commands.\n\nWebpage write output:\n{{ outputs.webpage_write | truncate(2500) }}\n\nMedia bind\/validate output:\n{{ outputs.media_bind_validate | truncate(3500) }}\n"
                },
                "skill": "filesystem",
                "depends_on": [
                    "media_bind_validate"
                ]
            },
            {
                "id": "delivery_guide",
                "kind": "llm_chat",
                "with": {
                    "task": "{{ inputs.language_instruction }}\n\nWrite a delivery guide containing:\n- output project path from config.awesome_webpage.output_dir: {{ inputs.get('config', {}).get('awesome_webpage', {}).get('output_dir') or (inputs.workspace_dir ~ '\/awesome-webpage-output') }}\/{{ outputs.project_slug | slugify }}\/project\n- how to open project\/index.html locally\n- how to replace images, audio, and video assets\n- how to edit content in index.html, style.css, and script.js\n- what was validated, including the deterministic media bind gate\n- any CONFIG_NEEDED items if config values were missing\n- do not claim a requested media modality is available unless the\n  media bind report says it is present and referenced\n\nValidation report:\n{{ outputs.quick_validate | truncate(5000) }}\n\nMedia bind report:\n{{ outputs.media_bind_validate | truncate(5000) }}",
                    "system": "You are the Delivery Guide stage for AwesomeWebpageMetaSkill.\nProduce concise user-facing run and edit instructions.\n"
                },
                "depends_on": [
                    "quick_validate"
                ]
            }
        ]
    },
    "description": "Build a local multimedia webpage project from a user topic, audience, language, style, and media preferences by framing requirements, researching, planning, acquiring media, generating files, packaging, validating, repairing, and delivering usage guidance.",
    "meta_priority": 65,
    "final_text_mode": "step:delivery_guide",
    "request_template": {
        "fields": [
            {
                "name": "topic",
                "label": "Topic",
                "label_en": "Topic",
                "label_zh": "网页主题"
            },
            {
                "name": "target_audience",
                "label": "Target audience",
                "label_en": "Target audience",
                "label_zh": "目标受众"
            },
            {
                "name": "output_language",
                "label": "Output language",
                "label_en": "Output language",
                "label_zh": "输出语言"
            },
            {
                "name": "visual_style",
                "label": "Visual style",
                "label_en": "Visual style",
                "label_zh": "视觉风格"
            },
            {
                "name": "media_preferences",
                "label": "Media preferences",
                "label_en": "Media preferences",
                "label_zh": "媒体偏好"
            }
        ],
        "outcome": "A packaged local multimedia webpage project with assets, validation notes, and usage guidance.",
        "outcome_en": "A packaged local multimedia webpage project with assets, validation notes, and usage guidance.",
        "outcome_zh": "打包好的本地多媒体网页项目，包含素材、验证说明和使用指南。"
    }
}

{
    "name": "cron",
    "always": false,
    "metadata": {
        "opensquilla": {
            "requires_tools": [
                "cron"
            ]
        }
    },
    "triggers": [
        "schedule",
        "recurring",
        "timer",
        "cron",
        "every",
        "reminder",
        "remind",
        "提醒",
        "每分钟",
        "每5分钟",
        "每天",
        "定时"
    ],
    "provenance": {
        "origin": "openclaw-derived",
        "license": "MIT",
        "upstream_url": "https:\/\/github.com\/openclaw\/openclaw",
        "maintained_by": "OpenSquilla"
    },
    "description": "Use when the user asks to schedule recurring tasks, one-off reminders, timers, or cron-style jobs through the OpenSquilla cron tool."
}

{
    "name": "docx",
    "homepage": "https:\/\/python-docx.readthedocs.io\/",
    "metadata": {
        "platform": {
            "emoji": "📘",
            "install": [
                {
                    "id": "python-docx",
                    "kind": "uv",
                    "label": "Install python-docx (uv pip)",
                    "package": "python-docx"
                }
            ],
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "entrypoint": {
        "args": [
            "--out",
            "{{ with.output_path }}"
        ],
        "parse": "text",
        "stdin": "{{ with.markdown }}",
        "command": "python {baseDir}\/scripts\/export_markdown_docx.py",
        "timeout": 60
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/word-docx",
        "maintained_by": "OpenSquilla"
    },
    "description": "Read, edit, or create Microsoft Word `.docx` files. Trigger this skill whenever the user mentions a Word document, .docx file, contract, report, brief, memo, or asks to extract text, modify an existing doc, generate one from a brief, or audit tracked changes. Three execution paths: text-and-structure extraction, in-place edit-by-run (preserves styles), and create-from-scratch with python-docx. Falls back to OOXML unzip-and-patch for layout work python-docx cannot reach."
}

{
    "name": "filesystem",
    "homepage": "https:\/\/clawhub.ai\/gtrusler\/clawdbot-filesystem",
    "metadata": {
        "clawdbot": {
            "emoji": "📁",
            "requires": {
                "bins": [
                    "node"
                ]
            }
        },
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "env": [],
                "bins": []
            },
            "capabilities": [
                "filesystem-read",
                "filesystem-write",
                "command-exec"
            ]
        }
    },
    "provenance": {
        "origin": "clawhub-mit",
        "license": "MIT",
        "upstream_url": "https:\/\/clawhub.ai\/gtrusler\/clawdbot-filesystem",
        "maintained_by": "OpenSquilla"
    },
    "description": "Advanced filesystem operations - listing, searching, batch processing, and directory analysis for Clawdbot"
}

{
    "name": "git-diff",
    "metadata": {
        "requires": {
            "bins": [
                "git"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--mode",
            "{{ with.mode | default('cached_fallback_worktree') }}",
            "--cwd",
            "{{ with.cwd | default('.') }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/git_diff.py",
        "timeout": 30
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Capture the current git diff (staged, working-tree, or staged file list) as text. Direct shell call for workflows that need repository diffs without an LLM agent loop."
}

{
    "name": "github",
    "metadata": {
        "openclaw": {
            "emoji": "🐙",
            "install": [
                {
                    "id": "brew",
                    "os": [
                        "darwin"
                    ],
                    "bins": [
                        "gh"
                    ],
                    "kind": "brew",
                    "label": "Install GitHub CLI (brew)",
                    "formula": "gh"
                },
                {
                    "id": "apt",
                    "bins": [
                        "gh"
                    ],
                    "kind": "apt",
                    "label": "Install GitHub CLI (apt)",
                    "package": "gh"
                }
            ],
            "requires": {
                "bins": [
                    "gh"
                ]
            }
        }
    },
    "provenance": {
        "origin": "openclaw-derived",
        "license": "MIT",
        "upstream_url": "https:\/\/github.com\/openclaw\/openclaw",
        "maintained_by": "OpenSquilla"
    },
    "description": "GitHub operations via `gh` CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating\/commenting on issues, (3) listing\/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available), bulk operations across many repos (script with gh api), or when gh auth is not configured."
}

{
    "name": "history-explorer",
    "metadata": {
        "requires": {
            "anyBins": [
                "python",
                "python3"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--query",
            "{{ with.query | truncate(512) }}",
            "--window-days",
            "{{ with.window_days | default('30') }}",
            "--include",
            "{{ with.include | join(',') if with.include is sequence and with.include is not string else with.include | default('co_occurrences,meta_usage,router_fixtures') }}",
            "--top-k",
            "10"
        ],
        "parse": "json",
        "command": "python {baseDir}\/scripts\/explore.py",
        "timeout": 30
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Query the per-turn DecisionEntry log for skill co-occurrence patterns, meta-skill usage stats, and the router fixture corpus. Returns a JSON summary suitable for downstream LLM consumption. Used by meta-skill-creator's harvest step but also useful standalone for 'which skills did I use most this week?'"
}

{
    "name": "html-coder",
    "homepage": "https:\/\/clawhub.ai\/jhauga\/html-coder",
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/jhauga\/html-coder",
        "maintained_by": "OpenSquilla"
    },
    "description": "Expert HTML development skill for building web pages, forms, and interactive content. Use when creating HTML documents, structuring web content, implementing semantic markup, adding forms and media, working with HTML5 APIs, or needing HTML templates, best practices, and accessibility guidance. Supports modern HTML5 standards and responsive design patterns.",
    "collaborators": [
        "make-skill-template",
        "finalize-agent-prompt"
    ]
}

{
    "name": "html-to-pdf",
    "homepage": "https:\/\/weasyprint.org\/",
    "metadata": {
        "platform": {
            "emoji": "📄",
            "install": [
                {
                    "id": "weasyprint",
                    "kind": "uv",
                    "label": "Install WeasyPrint (uv pip)",
                    "package": "weasyprint"
                }
            ],
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/generate-pdf",
        "maintained_by": "OpenSquilla"
    },
    "description": "Render HTML (with CSS) to a PDF file. Trigger when the user wants to export a styled report, invoice, label, or any HTML\/Jinja-rendered page to PDF. Uses WeasyPrint, which supports a meaningful subset of CSS Paged Media (page size, margins, headers\/footers, page-break-before\/after). Optional dependency — install via `pip install opensquilla[document-extras]` or `uv add weasyprint` because WeasyPrint pulls in native libraries (Pango, Cairo, fontconfig) that need OS-level packages."
}

{
    "name": "http-fetch",
    "metadata": {
        "requires": {
            "anyBins": [
                "python",
                "python3"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--url",
            "{{ with.url }}",
            "--method",
            "{{ with.method | default('GET') }}",
            "--timeout",
            "{{ with.timeout | default(30) }}",
            "--max-bytes",
            "{{ with.max_bytes | default(2000000) }}"
        ],
        "parse": "text",
        "stdin": "{{ with.body | default('') }}",
        "command": "python {baseDir}\/scripts\/http_fetch.py",
        "timeout": 60
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Fetch a URL via HTTP\/HTTPS and return the response body as text. Lightweight entrypoint replacement for `sub-agent` steps whose only job is a single GET\/POST. Supports GET (default), POST\/PUT\/DELETE with a stdin-piped body, configurable timeout, and a max-bytes cap — no LLM agent loop, no custom-header injection (request goes out with urllib defaults). Use for simple data-fetch steps in meta-skill DAGs; for crawling, JS-rendered pages, or complex auth chains use sub-agent + scrapling instead."
}

{
    "name": "latex-compile",
    "metadata": {
        "platform": {
            "emoji": "📄",
            "requires": {
                "anyBins": [
                    "xelatex"
                ]
            }
        }
    },
    "entrypoint": {
        "args": [
            "paper\/paper.tex"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/compile.py",
        "timeout": 120,
        "assemble": [
            {
                "into": "paper\/paper.tex",
                "from_template": "\\documentclass[11pt]{article}\n% xelatex is Unicode-native — do NOT use inputenc (incompatible)\n\\usepackage{amsmath}\n\\usepackage{amssymb}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{hyperref}\n\\title{ {{ inputs.user_message | truncate(80) }} }\n\\author{OpenSquilla meta-paper-write}\n\\date{\\today}\n\\begin{document}\n\\maketitle\n{{ outputs.draft_abstract }}\n{{ outputs.revised_body }}\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\end{document}"
            }
        ]
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Compile a LaTeX project (xelatex × bibtex × xelatex × xelatex) and report the log tail. Demo-only."
}

{
    "name": "memory",
    "always": false,
    "metadata": {
        "opensquilla": {
            "requires_tools": [
                "memory_search",
                "memory_get"
            ]
        }
    },
    "triggers": [
        "remember",
        "recall",
        "forget"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "upstream_url": "",
        "maintained_by": "OpenSquilla"
    },
    "description": "Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory\/**\/*.md."
}

{
    "name": "music-and-singing-studio",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "capabilities": [
                "network-read",
                "filesystem-write"
            ],
            "requires_tools": [
                "music_generate",
                "song_generate",
                "audio_provider_capabilities"
            ]
        }
    },
    "triggers": [
        "generate music",
        "music generation",
        "song generate",
        "lyrics to song",
        "唱歌",
        "生成歌曲",
        "生成音乐",
        "BGM"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Generate instrumental music, background beds, jingles, or sung songs with lyrics through OpenSquilla audio tools. Use when the user asks for BGM, music generation, 唱歌, 生成歌曲, lyrics to song, or a playable music audio artifact."
}

{
    "name": "nano-banana-pro-openrouter",
    "homepage": "https:\/\/clawhub.ai\/skills\/nano-banana-pro-openrouter",
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "requires": {
                "env": [],
                "bins": [
                    "python3"
                ],
                "config": [
                    "awesome_webpage.openrouter.api_key_env",
                    "awesome_webpage.openrouter.base_url",
                    "awesome_webpage.openrouter.models.image_generation",
                    "awesome_webpage.output_dir"
                ]
            },
            "capabilities": [
                "network-write",
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "env": {
            "{{ with.api_key_env | default('OPENROUTER_API_KEY') }}": "{{ with.api_key | default('') }}"
        },
        "args": [
            "--model",
            "{{ with.model | default('google\/gemini-3-pro-image-preview') }}",
            "--base-url",
            "{{ with.base_url | default('https:\/\/openrouter.ai\/api\/v1') }}",
            "--api-key-env",
            "{{ with.api_key_env | default('OPENROUTER_API_KEY') }}",
            "--output-dir",
            "{{ with.output_dir }}",
            "--filename",
            "{{ with.filename | default('image.png') }}",
            "--resolution",
            "{{ with.resolution | default('1K') }}",
            "--max-images",
            "{{ with.max_images | default('6') }}",
            "--local-path-prefix",
            "{{ with.local_path_prefix | default('project\/assets\/images') }}"
        ],
        "parse": "text",
        "stdin": "{{ with.payload | default(with.prompt | default(inputs.user_message)) }}",
        "command": "python {baseDir}\/scripts\/openrouter_image.py",
        "timeout": 300
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/skills\/nano-banana-pro-openrouter",
        "maintained_by": "OpenSquilla"
    },
    "description": "Deterministic OpenRouter image generation adapter for Nano Banana Pro \/ Gemini image models. Use as skill_exec when a meta-skill needs local image files and structured IMAGE_READY records without spawning an LLM agent.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "nano-banana-pro",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "envAny": [
                    "OPENROUTER_API_KEY"
                ],
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "network-read",
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--prompt",
            "{{ with.prompt }}",
            "--filename",
            "{{ with.filename }}",
            "--aspect-ratio",
            "{{ with.aspect_ratio | default('1:1') }}",
            "--image-size",
            "{{ with.image_size | default('1K') }}",
            "--model",
            "{{ with.model | default('google\/gemini-3.1-flash-image-preview') }}",
            "--max-retries",
            "{{ with.max_retries | default(0) }}",
            "--fallback-model",
            "{{ with.fallback_model | default('') }}",
            "--placeholder-on-fail",
            "{{ with.placeholder_on_fail | default('no') }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/generate_image.py",
        "timeout": 600
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/steipete\/nano-banana-pro",
        "maintained_by": "OpenSquilla",
        "modifications": "Rewired from Google Gemini SDK to OpenRouter \/v1\/chat\/completions; pure-stdlib HTTP client.",
        "upstream_version": "1.0.1"
    },
    "description": "Generate or edit a single image via OpenRouter (google\/gemini-3.1-flash-image-preview by default). Accepts a text prompt and optional --input-image for image-to-image editing. Trigger when the user asks for an AI image, illustration, concept art, product render, or wants to modify an existing image."
}

{
    "name": "nano-pdf",
    "homepage": "https:\/\/pypi.org\/project\/nano-pdf\/",
    "metadata": {
        "openclaw": {
            "emoji": "📄",
            "install": [
                {
                    "id": "uv",
                    "bins": [
                        "nano-pdf"
                    ],
                    "kind": "uv",
                    "label": "Install nano-pdf (uv)",
                    "package": "nano-pdf"
                }
            ],
            "requires": {
                "bins": [
                    "nano-pdf"
                ]
            }
        }
    },
    "provenance": {
        "origin": "openclaw-derived",
        "license": "MIT",
        "upstream_url": "https:\/\/github.com\/openclaw\/openclaw",
        "maintained_by": "OpenSquilla"
    },
    "description": "Edit PDFs with natural-language instructions using the nano-pdf CLI."
}

{
    "name": "openrouter-video-generator",
    "homepage": "",
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "requires": {
                "env": [],
                "config": [
                    "awesome_webpage.openrouter.api_key",
                    "awesome_webpage.openrouter.api_key_env",
                    "awesome_webpage.openrouter.base_url",
                    "awesome_webpage.openrouter.models.video_generation",
                    "awesome_webpage.output_dir"
                ]
            },
            "capabilities": [
                "network-write",
                "filesystem-read",
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "env": {
            "{{ with.api_key_env | default('OPENROUTER_API_KEY') }}": "{{ with.api_key | default('') }}"
        },
        "args": [
            "--model",
            "{{ with.model | default('bytedance\/seedance-2.0-fast') }}",
            "--base-url",
            "{{ with.base_url | default('https:\/\/openrouter.ai\/api\/v1') }}",
            "--api-key-env",
            "{{ with.api_key_env | default('OPENROUTER_API_KEY') }}",
            "--output-dir",
            "{{ with.output_dir }}",
            "--filename",
            "{{ with.filename | default('intro.mp4') }}",
            "--duration",
            "{{ with.duration | default(10) }}",
            "--aspect-ratio",
            "{{ with.aspect_ratio | default('16:9') }}"
        ],
        "parse": "text",
        "stdin": "{{ with.prompt | default(inputs.user_message) }}",
        "command": "python {baseDir}\/scripts\/openrouter_video.py",
        "timeout": 420
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Generate or declare a configured OpenRouter video asset for AwesomeWebpageMetaSkill. Use only when the meta skill needs a local webpage video and the provider, model, API key, and output directory come from config.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "paper-abstract-author",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Write the abstract after the paper body has been revised, using the final claims and evidence."
}

{
    "name": "paper-citation-planner",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Map paper claims to available BibTeX citation keys before section drafting."
}

{
    "name": "paper-experiment-stub",
    "metadata": {
        "platform": {
            "emoji": "🧪",
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "entrypoint": {
        "args": [
            "--topic",
            "{{ inputs.user_message }}",
            "--out",
            "paper\/results.csv"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/gen_results.py",
        "timeout": 15
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Demo-only stub experiment for meta-paper-write: generates a deterministic results.csv seeded by topic hash. Not real science."
}

{
    "name": "paper-outline-author",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Author a 5-section paper outline (abstract \/ introduction \/ method \/ results \/ discussion) for a research topic, citing supplied reference keys when relevant."
}

{
    "name": "paper-preference-planner",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Extract paper-writing preferences from a user request before research and drafting, choosing direct generation defaults when the request does not require a preference interview."
}

{
    "name": "paper-refbib-stub",
    "metadata": {
        "platform": {
            "emoji": "📚",
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "entrypoint": {
        "args": [
            "--out",
            "paper\/references.bib"
        ],
        "parse": "text",
        "stdin": "{{ outputs.search_papers }}",
        "command": "python {baseDir}\/scripts\/json_to_bib.py",
        "timeout": 10
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Convert multi-search-engine JSON to a minimal BibTeX file (@misc{} entries). Demo-only.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "paper-revision-author",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written."
}

{
    "name": "paper-section-author",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Write one publication-style research-paper section as a bounded, citation-grounded LaTeX fragment from a writing plan, outline, citation plan, and optional figure\/table context.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "paper-source-curator",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Curate search results and BibTeX entries into a reliable source pack for a long research paper."
}

{
    "name": "pptx",
    "homepage": "https:\/\/python-pptx.readthedocs.io\/",
    "metadata": {
        "platform": {
            "emoji": "📊",
            "install": [
                {
                    "id": "python-pptx",
                    "kind": "uv",
                    "label": "Install python-pptx (uv pip)",
                    "package": "python-pptx"
                },
                {
                    "id": "pptxgenjs",
                    "bins": [
                        "node"
                    ],
                    "kind": "npm",
                    "label": "Install PptxGenJS (npm, optional — only for from-scratch JS path)",
                    "package": "pptxgenjs"
                },
                {
                    "id": "libreoffice-darwin",
                    "os": [
                        "darwin"
                    ],
                    "bins": [
                        "soffice"
                    ],
                    "kind": "brew",
                    "label": "Install LibreOffice for visual QA (brew)",
                    "formula": "libreoffice"
                },
                {
                    "id": "poppler-darwin",
                    "os": [
                        "darwin"
                    ],
                    "bins": [
                        "pdftoppm"
                    ],
                    "kind": "brew",
                    "label": "Install Poppler (pdftoppm) for slide-image QA (brew)",
                    "formula": "poppler"
                }
            ],
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/ivangdavila\/powerpoint-pptx",
        "maintained_by": "OpenSquilla"
    },
    "description": "Read, edit, or create PowerPoint .pptx files. Trigger this skill whenever the user mentions a deck, slides, slide deck, presentation, or a `.pptx` filename — whether the goal is to extract text, modify an existing deck, build one from scratch, or prepare slides for review. Three execution paths are supported: text extraction (always available), template editing (unzip → patch slide XML → repack), and creation from scratch (python-pptx for Python or PptxGenJS for Node)."
}

{
    "name": "seedance-2-prompt",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "envAny": [
                    "OPENROUTER_API_KEY",
                    "ARK_API_KEY"
                ],
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "network-read",
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--prompt",
            "{{ with.prompt }}",
            "--filename",
            "{{ with.filename }}",
            "--provider",
            "{{ with.provider | default('openrouter') }}",
            "--aspect-ratio",
            "{{ with.aspect_ratio | default('9:16') }}",
            "--duration",
            "{{ with.duration | default(5) }}",
            "--resolution",
            "{{ with.resolution | default('720p') }}",
            "--model",
            "{{ with.model | default('') }}",
            "--input-image",
            "{{ with.input_image | default('') }}",
            "--input-reference",
            "{{ with.input_reference | default('') }}",
            "--input-reference",
            "{{ with.input_reference_2 | default('') }}",
            "--max-retries",
            "{{ with.max_retries | default(0) }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/generate_video.py",
        "timeout": 1500
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/dandysuper\/seedance-2-prompt-engineering-skill",
        "maintained_by": "OpenSquilla",
        "modifications": "Added scripts\/generate_video.py with dual-provider support: OpenRouter async \/videos API and Volcengine ARK \/ BytePlus ModelArk \/contents\/generations\/tasks. References kept from upstream.",
        "upstream_version": "2.0.0"
    },
    "description": "Render a single 3-15s video clip via Seedance 2.0. Supports two backends: OpenRouter (default, model bytedance\/seedance-2.0) and the official Volcengine ARK \/ BytePlus ModelArk endpoint (model doubao-seedance-2-0-260128 \/ dreamina-seedance-2-0-260128). Accepts a structured English video prompt, optional first-frame image, and optional identity\/style reference image. Trigger when the user asks for AI video clip generation, 分镜视频, seedance, or wants a short cinematic shot from a prompt + frame."
}

{
    "name": "skill-creator-linter",
    "metadata": {
        "requires": {
            "anyBins": [
                "python",
                "python3"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--skill-md-stdin",
            "--gates",
            "G1,G2"
        ],
        "parse": "json",
        "stdin": "{{ with.skill_md }}",
        "command": "python {baseDir}\/scripts\/lint.py",
        "timeout": 30
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal tool (not user-invocable). Called by meta-skill-creator as a DAG step (kind: agent) to lint a candidate meta-skill SKILL.md against G1 (parse + reference check + xml_escape grep + structural lint) and G2 (scheduler dry-run with stub executors). Deterministic, sub-second, no LLM. Returns JSON diagnostics.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "skill-creator-proposals",
    "metadata": {
        "requires": {
            "anyBins": [
                "python",
                "python3"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--action",
            "{{ with.action | default('write_proposal') }}",
            "--skill-md-inline",
            "{{ with.skill_md | default('') }}",
            "--lint-result",
            "{{ with.lint_result | default('{}') }}",
            "--smoke-result",
            "{{ with.smoke_result | default('{}') }}",
            "--creator-mode",
            "{{ with.creator_mode | default('') }}",
            "--acceptance-result",
            "{{ with.acceptance_result | default('') }}",
            "--runtime-e2e-result",
            "{{ with.runtime_e2e_result | default('') }}"
        ],
        "parse": "json",
        "command": "python {baseDir}\/scripts\/proposals.py",
        "timeout": 30
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal tool (not user-invocable). Called by meta-skill-creator's persist step and by `opensquilla meta accept` CLI (Phase 2) to manage `~\/.opensquilla\/proposals\/`: write_proposal \/ list \/ accept. Returns JSON.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "skill-creator-smoke-test",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal tool (not user-invocable). Called by meta-skill-creator as a DAG step (kind: agent) to run G3 (positive smoke) and G4 (negative smoke) gates against a candidate meta-skill SKILL.md. Cross-vendor: fixture-generation LLM != classifier LLM. Returns JSON.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "srt-from-script",
    "metadata": {
        "opensquilla": {
            "risk": "low",
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--output",
            "{{ with.output_path }}",
            "--gap-ms",
            "{{ with.gap_ms | default(200) }}",
            "--leading-offset-ms",
            "{{ with.leading_offset_ms | default(0) }}"
        ],
        "parse": "text",
        "stdin": "{{ with.script }}",
        "command": "python {baseDir}\/scripts\/build_srt.py",
        "timeout": 30
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Build an SRT subtitle file from a 3-shot short-drama script (ai-video-script OUTPUT FORMAT). Reads each SHOT_N block's DURATION_S + VOICEOVER, emits cumulative-timestamped SRT cues. Pure text-processing, no LLM, no network. Used by meta-short-drama between merge and the final subtitle-burn step.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "stack-trace-generic-probe",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal helper for meta-stack-trace-investigator. Use when a stack trace language is unknown and the workflow needs language-neutral failure-contract checks, reproducer guidance, and patch targets.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "stack-trace-go-probe",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal helper for meta-stack-trace-investigator. Use when a Go panic or stack trace needs Go-specific nil\/error checks, go test reproducer guidance, and patch targets.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "stack-trace-js-probe",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal helper for meta-stack-trace-investigator. Use when a JavaScript or TypeScript stack trace needs npm\/node\/tsc-specific checks, reproduction guidance, and patch targets.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "stack-trace-python-probe",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal helper for meta-stack-trace-investigator. Use when a Python traceback needs Python-specific root-cause checks, pytest reproducer guidance, and defensive patch targets.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "stack-trace-rust-probe",
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Internal helper for meta-stack-trace-investigator. Use when a Rust panic or backtrace needs Rust-specific Result\/Option checks, cargo test guidance, and patch targets.",
    "user-invocable": false,
    "disable-model-invocation": true
}

{
    "name": "subtitle-burner",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "bins": [
                    "ffmpeg"
                ],
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "filesystem-write",
                "process-control"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--input",
            "{{ with.input }}",
            "--subtitles",
            "{{ with.subtitles }}",
            "--output",
            "{{ with.output }}",
            "--font",
            "{{ with.font | default('Microsoft YaHei,SimHei,Arial Unicode MS,Arial') }}",
            "--font-size",
            "{{ with.font_size | default(42) }}",
            "--margin-v",
            "{{ with.margin_v | default(80) }}",
            "--play-res",
            "{{ with.play_res | default('auto') }}",
            "--crf",
            "{{ with.crf | default(20) }}",
            "--preset",
            "{{ with.preset | default('medium') }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/burn.py",
        "timeout": 600
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Burn an SRT subtitle file into an MP4 via ffmpeg's subtitles filter (libass). Single-pass re-encode of video; audio copied as-is. CJK-friendly font fallback chain (Microsoft YaHei → SimHei → Arial Unicode MS → Arial). Used by meta-short-drama as the final subtitling step after merge."
}

{
    "name": "swe-bench",
    "metadata": {
        "platform": {
            "emoji": "🧪",
            "install": [
                {
                    "id": "swebench-extra",
                    "kind": "uv",
                    "label": "Install SWE-bench extras (uv pip)",
                    "package": "opensquilla[swebench]"
                }
            ],
            "requires": {
                "env": [
                    "OPENROUTER_API_KEY"
                ]
            }
        }
    },
    "triggers": [
        "swe-bench",
        "swebench",
        "SWE-bench",
        "跑一道题",
        "解一道 SWE",
        "benchmark instance"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Run SWE-bench instances with an OpenSquilla agent inside the official Docker images. Trigger when the user wants to run\/solve\/evaluate a SWE-bench instance (e.g. 'run django__django-16429', 'test OpenSquilla on SWE-bench', '跑一道 SWE-bench 题'), benchmark the agent on SWE-bench_Verified or SWE-bench_Multilingual, or check whether a generated patch resolves an instance. Optional dependency — install via `pip install opensquilla[swebench]`; also needs the docker CLI and an OPENROUTER_API_KEY."
}

{
    "name": "text-file-read",
    "metadata": {
        "opensquilla": {
            "risk": "low",
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "filesystem-read"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--input",
            "{{ with.input }}",
            "--max-bytes",
            "{{ with.max_bytes | default(200000) }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/read.py",
        "timeout": 10
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Read a UTF-8 text file and emit its raw content on stdout. Tiny helper for meta-skills that need to round-trip an artefact through disk so the user can hand-edit it between steps (e.g. tweak script.txt during a review pause). Unlike the builtin read_file tool — which returns line-numbered output for model display — this returns bytes verbatim, suitable for downstream parsers."
}

{
    "name": "title-card-image",
    "metadata": {
        "opensquilla": {
            "risk": "low",
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "filesystem-write"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--text",
            "{{ with.text }}",
            "--output",
            "{{ with.output }}",
            "--subtitle",
            "{{ with.subtitle | default('') }}",
            "--background",
            "{{ with.background | default('#101018') }}",
            "--text-color",
            "{{ with.text_color | default('#ffffff') }}",
            "--font-size",
            "{{ with.font_size | default(96) }}",
            "--subtitle-size",
            "{{ with.subtitle_size | default(36) }}",
            "--width",
            "{{ with.width | default(720) }}",
            "--height",
            "{{ with.height | default(1280) }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/render.py",
        "timeout": 30
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Render a static title \/ ending card PNG with Pillow. Centered headline + optional subtitle on a solid-colour background. CJK-friendly font fallback (Microsoft YaHei → SimHei → Songti → Noto CJK → bitmap). Pure deterministic, no LLM, no network. Used by meta-short-drama for opening and closing cards."
}

{
    "name": "video-merger",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "bins": [
                    "ffmpeg",
                    "ffprobe"
                ],
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "filesystem-write",
                "process-control"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--input",
            "{{ with.input_dir }}",
            "--output",
            "{{ with.output_path }}",
            "--mode",
            "{{ with.mode | default('full') }}",
            "--transition",
            "{{ with.transition | default(0.5) }}",
            "--fps",
            "{{ with.fps | default(24) }}",
            "--crf",
            "{{ with.crf | default(22) }}",
            "--preset",
            "{{ with.preset | default('medium') }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/merge.py",
        "timeout": 600
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/machunlin\/video-merger",
        "maintained_by": "OpenSquilla",
        "upstream_version": "1.1.0"
    },
    "description": "Concatenate a directory of numbered MP4 segments (1_*.mp4, 2_*.mp4, ...) into one MP4 with optional fade transitions, unified resolution\/fps\/codec. Pure ffmpeg wrapper, no LLM. Trigger when a workflow has produced several short clips that need stitching into a final reel."
}

{
    "name": "video-still-animator",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "requires": {
                "bins": [
                    "ffmpeg"
                ],
                "anyBins": [
                    "python",
                    "python3"
                ]
            },
            "capabilities": [
                "filesystem-write",
                "process-control"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "--input",
            "{{ with.input_image }}",
            "--output",
            "{{ with.output_path }}",
            "--duration",
            "{{ with.duration | default(5) }}",
            "--width",
            "{{ with.width | default(720) }}",
            "--height",
            "{{ with.height | default(1280) }}",
            "--fps",
            "{{ with.fps | default(24) }}",
            "--zoom-rate",
            "{{ with.zoom_rate | default(0.0015) }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/animate.py",
        "timeout": 120
    },
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "description": "Turn a single still image (PNG\/JPG) into a short MP4 with a slow Ken-Burns zoom and a silent audio track. Pure ffmpeg wrapper. Designed as the on_failure substitute for AI video-gen steps that get blocked by content moderation: when seedance refuses, this skill emits a valid replacement clip from the already-generated still so a downstream merge can still produce a complete deliverable."
}

{
    "name": "voice-clone-lab",
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "network-read",
                "filesystem-write"
            ],
            "requires_tools": [
                "voice_clone",
                "audio_provider_capabilities"
            ]
        }
    },
    "triggers": [
        "voice clone",
        "clone voice",
        "克隆音色",
        "复刻声音",
        "声音克隆"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Create and register cloned voices for later TTS only when the speaker has explicit consent. Use when the user asks for voice clone, clone voice, 克隆音色, 复刻声音, or wants a reusable voice_id."
}

{
    "name": "voice-conversion-studio",
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "network-read",
                "filesystem-write"
            ],
            "requires_tools": [
                "voice_convert",
                "audio_provider_capabilities"
            ]
        }
    },
    "triggers": [
        "voice conversion",
        "voice convert",
        "voice changer",
        "音色转换",
        "换声",
        "变声"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Convert a local source recording into an authorized target voice. Use when the user asks for voice conversion, voice changer, 换声, 变声, 音色转换, or converting existing narration to another approved voice."
}

{
    "name": "voiceover-studio",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "capabilities": [
                "network-read",
                "filesystem-write"
            ],
            "requires_tools": [
                "tts",
                "voice_search",
                "audio_provider_capabilities"
            ]
        }
    },
    "triggers": [
        "tts",
        "text to speech",
        "voiceover",
        "配音",
        "旁白",
        "口播",
        "生成语音"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "Generate narration, product voiceover, IVR prompts, podcast reads, or short-video VOICEOVER audio through OpenSquilla audio tools. Use when the user asks for TTS, 配音, 旁白, 口播, audio narration, or wants script text turned into a playable audio artifact."
}

{
    "name": "weather",
    "homepage": "https:\/\/wttr.in\/:help",
    "metadata": {
        "openclaw": {
            "emoji": "☔",
            "install": [
                {
                    "id": "brew",
                    "os": [
                        "darwin"
                    ],
                    "bins": [
                        "curl"
                    ],
                    "kind": "brew",
                    "label": "Install curl (brew)",
                    "formula": "curl"
                }
            ],
            "requires": {
                "bins": [
                    "curl"
                ]
            }
        }
    },
    "entrypoint": {
        "args": [
            "--location",
            "{{ with.location | default(inputs.user_message) }}",
            "--days",
            "{{ with.days | default(3) }}",
            "--timeout",
            "{{ with.timeout | default(8) }}",
            "--max-chars",
            "{{ with.max_chars | default(2500) }}"
        ],
        "parse": "json",
        "command": "python {baseDir}\/scripts\/weather_fetch.py",
        "timeout": 15
    },
    "provenance": {
        "origin": "openclaw-derived",
        "license": "MIT",
        "upstream_url": "https:\/\/github.com\/openclaw\/openclaw",
        "maintained_by": "OpenSquilla"
    },
    "description": "Get current weather and forecasts via wttr.in or Open-Meteo. Use when: user asks about weather, temperature, or forecasts for any location. NOT for: historical weather data, severe weather alerts, or detailed meteorological analysis. No API key needed."
}

{
    "name": "web-search",
    "homepage": "https:\/\/clawhub.ai\/billyutw\/web-search",
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "capabilities": [
                "network-read"
            ]
        }
    },
    "entrypoint": {
        "args": [
            "{{ with.query | default(inputs.user_message) }}",
            "--type",
            "{{ with.type | default('web') }}",
            "--max-results",
            "{{ with.max_results | default('10') }}",
            "--format",
            "{{ with.format | default('text') }}"
        ],
        "parse": "text",
        "command": "python {baseDir}\/scripts\/search.py",
        "timeout": 45
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/billyutw\/web-search",
        "maintained_by": "OpenSquilla"
    },
    "description": "This skill should be used when users need to search the web for information, find current content, look up news articles, search for images, or find videos. It uses DuckDuckGo's search API to return results in clean, formatted output (text, markdown, or JSON). Use for research, fact-checking, finding recent information, or gathering web resources."
}

{
    "name": "xlsx",
    "homepage": "https:\/\/openpyxl.readthedocs.io\/",
    "metadata": {
        "platform": {
            "emoji": "📗",
            "install": [
                {
                    "id": "openpyxl",
                    "kind": "uv",
                    "label": "Install openpyxl (uv pip)",
                    "package": "openpyxl"
                }
            ],
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/excel-xlsx",
        "maintained_by": "OpenSquilla"
    },
    "description": "Read, edit, or create Microsoft Excel `.xlsx` workbooks. Trigger this skill whenever the user mentions a spreadsheet, .xlsx file, workbook, sheet, formula, pivot table, or asks to extract tabular data, modify a sheet, or build a workbook from rows. Three execution paths: structured inspection, in-place cell edits, and create-from-scratch via openpyxl. Values starting with `=` are written as formulas; everything else is a literal value with type preserved (int \/ float \/ str \/ datetime)."
}

{
    "kind": "meta",
    "name": "meta-arxiv-daily-digest-deck",
    "always": false,
    "triggers": [
        "arxiv daily digest",
        "arxiv digest deck",
        "arxiv 日报",
        "arxiv 晨刊"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "fetch_arxiv",
                "kind": "agent",
                "with": {
                    "task": "Fetch the latest papers from arXiv's public API.\n\nDefault query: cs.CL OR cs.AI, sorted by submittedDate descending,\nmax_results=6. If the user's invocation contains a category override,\nhonor it (look for `cs.XX` patterns in the message).\n\nUser invocation:\n{{ inputs.user_message | xml_escape | truncate(200) }}\n\nURL template: `http:\/\/export.arxiv.org\/api\/query?search_query=<query>&sortBy=submittedDate&sortOrder=descending&max_results=6`\n\nUse the available HTTP fetch tool. Parse the Atom XML response and\nextract for each entry: id (with version), title, summary (the abstract),\nauthors (up to 5), pdf URL.\n\nReply with ONLY a JSON array on one line, no preamble:\n  [{\"id\":\"...\", \"title\":\"...\", \"abstract\":\"...\", \"authors\":[...], \"pdf_url\":\"...\"}, ...]\n\nIf the fetch fails (HTTP error, parse error, empty result), reply with\nexactly: FETCH_FAILED: <one-line cause>\n"
                },
                "skill": "sub-agent"
            },
            {
                "id": "digest_papers",
                "kind": "agent",
                "with": {
                    "task": "Write a structured digest for each paper. Skip if upstream is\nFETCH_FAILED — reply with `DIGEST_SKIPPED` in that case.\n\nPapers JSON from upstream:\n---\n{{ outputs.fetch_arxiv | truncate(10000) }}\n---\n\nFor each paper, output this exact block (separate papers with a line of `---`):\n\n## <Paper Title>\n**Authors**: <first 3 author names, then et al. if more>\n**arXiv ID**: <id>\n**Core claim** (1 sentence): <claim>\n**Method summary** (2-3 sentences): <how>\n**Key numbers** (if present): <metric: value, metric: value>\n**Why it matters** (1 sentence): <relevance>\n"
                },
                "skill": "summarize",
                "depends_on": [
                    "fetch_arxiv"
                ]
            },
            {
                "id": "render_deck",
                "kind": "agent",
                "with": {
                    "task": "Render the per-paper digest below into a PPTX deck. One slide per\npaper. Slide title = paper title. Slide body = the rest of that\npaper's digest block. Title slide first with text\n`arXiv Daily Digest — {{ inputs.user_message | xml_escape | truncate(80) }}`.\n\nDigest body:\n---\n{{ outputs.digest_papers | truncate(12000) }}\n---\n\nSave to: `{{ inputs.workspace_dir }}\/arxiv-daily\/digest.pptx`\n(overwrite OK; cron mode keeps only the latest, manual fire uses\nthe same name and the user can rename after if needed). Create\nthe `arxiv-daily\/` subdirectory if missing.\n\nReply with the absolute output path on a single line, no preamble.\n\nIf the digest is `DIGEST_SKIPPED`, reply: `RENDER_SKIPPED`."
                },
                "skill": "pptx",
                "depends_on": [
                    "digest_papers"
                ]
            },
            {
                "id": "persist",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/arxiv-daily.md",
                    "content": "=== arxiv-daily digest ===\ninvocation: {{ inputs.user_message | xml_escape | truncate(200) }}\ndeck: {{ outputs.render_deck | truncate(200) }}\ndigest:\n{{ outputs.digest_papers | truncate(8000) }}"
                },
                "depends_on": [
                    "digest_papers",
                    "render_deck"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Fetch the day's top arXiv submissions in a chosen category, write a structured per-paper digest, render the digest as a PPTX deck (one slide per paper), and persist the digest to long-term memory. Use for a daily 'arxiv morning briefing' — manual fire or cron-scheduled.",
    "meta_priority": 55
}

{
    "kind": "meta",
    "name": "meta-codereview-current-diff",
    "always": false,
    "triggers": [
        "multi-reviewer diff",
        "codereview my diff",
        "三路 review",
        "审查当前 diff"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "read_diff",
                "kind": "skill_exec",
                "with": {
                    "cwd": "{{ inputs.workspace_dir | default('.') }}",
                    "mode": "cached_fallback_worktree"
                },
                "skill": "git-diff"
            },
            {
                "id": "review_safety",
                "kind": "llm_chat",
                "with": {
                    "task": "You are the *safety reviewer* for a 3-reviewer codereview bundle.\nApply ONLY the rules below; do not invent additional concerns.\n\nDiff under review:\n---\n{{ outputs.read_diff | truncate(8000) }}\n---\n\nRules (in priority order):\n  1. CRITICAL if the diff introduces SQL injection (string\n     concatenation into SQL, unparameterized queries via .format\n     or f-strings into execute\/query).\n  2. CRITICAL if the diff introduces shell-injection\n     (subprocess with shell=True + user input, or os.system on\n     unvalidated strings).\n  3. CRITICAL if the diff hardcodes a credential (sk-…, ghp_…,\n     AKIA…, private key, password=… string literal).\n  4. WARNING if the diff templates user-controlled strings into\n     tool args \/ prompts WITHOUT `xml_escape` (G1.6 contract).\n  5. CLEAR if none of the above apply.\n\nReply with EXACTLY one line, no preamble:\n  CRITICAL: <one-sentence reason>\n  WARNING: <one-sentence reason>\n  CLEAR: no safety concerns found\n",
                    "system": "You are a precise code safety reviewer. Reply with exactly one requested verdict line."
                },
                "depends_on": [
                    "read_diff"
                ]
            },
            {
                "id": "review_tests",
                "kind": "llm_chat",
                "with": {
                    "task": "You are the *test-coverage reviewer* for a 3-reviewer codereview\nbundle. Apply ONLY the rules below.\n\nDiff under review:\n---\n{{ outputs.read_diff | truncate(8000) }}\n---\n\nRules:\n  1. MISSING_TESTS if the diff adds a new public function \/ class \/\n     protocol under `src\/` and does NOT add a corresponding test\n     under `tests\/`.\n  2. MISSING_TESTS if the diff is a bug fix (commit message style\n     `fix(...):`) and does NOT add a regression test.\n  3. PASS if refactor-only (no behaviour change), even without\n     new tests.\n  4. PASS if the diff is doc-only or config-only.\n\nReply with EXACTLY one line, no preamble:\n  MISSING_TESTS: <which functions\/classes lack tests>\n  PASS: tests adequate (or n\/a)\n",
                    "system": "You are a precise test-coverage reviewer. Reply with exactly one requested verdict line."
                },
                "depends_on": [
                    "read_diff"
                ]
            },
            {
                "id": "review_style",
                "kind": "llm_chat",
                "with": {
                    "task": "You are the *style \/ idiom reviewer* for a 3-reviewer codereview\nbundle. Look for project-specific anti-patterns.\n\nDiff under review:\n---\n{{ outputs.read_diff | truncate(8000) }}\n---\n\nAnti-patterns to flag (do not invent others):\n  - Functions > 80 source lines\n  - Conditional nesting > 4 deep\n  - Magic numeric literals without a named constant\n  - Stale TODO\/FIXME comments left in shipped code\n  - Bare `except:` clauses (no exception class)\n  - `print()` calls instead of structlog `log.info(...)`\n\nReply with EXACTLY one line, no preamble:\n  ANTIPATTERNS: <one-line list with line refs>\n  CLEAN: no style issues found\n",
                    "system": "You are a precise style reviewer. Reply with exactly one requested verdict line."
                },
                "depends_on": [
                    "read_diff"
                ]
            },
            {
                "id": "arbitrate",
                "kind": "llm_chat",
                "with": {
                    "task": "Three reviewers ran on the diff:\n  - Safety: {{ outputs.review_safety }}\n  - Tests:  {{ outputs.review_tests }}\n  - Style:  {{ outputs.review_style }}\n\nApply STRICTLY (higher rule wins; do NOT mix or soften):\n  1. If Safety starts with \"CRITICAL\" → final verdict BLOCK.\n     Pass through the safety reviewer's reason verbatim.\n  2. Else if Safety starts with \"WARNING\" OR Tests starts with\n     \"MISSING_TESTS\" → final verdict BLOCK_WITH_OVERRIDE\n     (user may proceed with explicit acknowledgement; pass the\n     warning(s) verbatim).\n  3. Else → PASS_WITH_NOTES. If Style starts with \"ANTIPATTERNS\",\n     append the style notes; if all three are clean, write\n     \"clean\" instead.\n\nReply with EXACTLY this structure on the first line, then\nadditional lines as needed:\n  BLOCK: <safety critical reason>\n  BLOCK_WITH_OVERRIDE: <warning(s); user must confirm>\n  PASS_WITH_NOTES: <style notes; or \"clean\">",
                    "system": "You arbitrate code review verdicts. Follow the priority rules exactly."
                },
                "depends_on": [
                    "review_safety",
                    "review_tests",
                    "review_style"
                ]
            }
        ]
    },
    "description": "Read the current uncommitted diff, run three independent reviewers (safety + tests-coverage + style) in parallel, then arbitrate a single BLOCK \/ BLOCK_WITH_OVERRIDE \/ PASS_WITH_NOTES verdict. Use before commit when you want a multi-perspective second-opinion instead of a single-reviewer agent loop.",
    "meta_priority": 65,
    "final_text_mode": "step:arbitrate"
}

{
    "kind": "meta",
    "name": "meta-compliance-audit-bundle",
    "always": false,
    "triggers": [
        "合规审计",
        "compliance audit",
        "audit bundle",
        "GDPR 自评"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "research",
                "with": {
                    "rounds": 1,
                    "question": "{{ inputs.user_message | xml_escape | truncate(512) }}"
                },
                "skill": "deep-research"
            },
            {
                "id": "report",
                "with": {
                    "body": "{{ outputs.research }}",
                    "title": "Compliance Report — {{ inputs.user_message | xml_escape | truncate(96) }}"
                },
                "skill": "docx",
                "depends_on": [
                    "research"
                ]
            },
            {
                "id": "archive",
                "with": {
                    "html": "<!DOCTYPE html>\n<html><head><meta charset=\"utf-8\"><title>Audit Archive — {{ inputs.user_message | xml_escape | truncate(96) }}<\/title><\/head>\n<body>\n  <h1>Audit Archive: {{ inputs.user_message | xml_escape | truncate(128) }}<\/h1>\n  <article>{{ outputs.research | xml_escape }}<\/article>\n<\/body><\/html>\n",
                    "page_size": "A4"
                },
                "skill": "html-to-pdf",
                "depends_on": [
                    "research"
                ]
            },
            {
                "id": "memorize",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/compliance-audit.md",
                    "content": "{{ outputs.research }}"
                },
                "depends_on": [
                    "research"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Auditable compliance bundle: deep-research with citations → signable .docx report → read-only PDF archive → memory note of audit findings.",
    "meta_priority": 45
}

{
    "kind": "meta",
    "name": "meta-diagram-triangulation",
    "always": false,
    "triggers": [
        "diagram triangulation",
        "triangulate diagrams",
        "架构三视图",
        "出双视图架构图"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "scan_repo",
                "kind": "agent",
                "with": {
                    "task": "Scan the target path identified in the user's invocation for\narchitectural structure. Extract:\n  * Top-level modules \/ packages under the target path\n  * Inter-module import dependencies (who imports who)\n  * Hotspot files (most-changed in the last 90 days)\n  * Public surface: classes, functions, protocols exported via __init__.py\n\nUser invocation (target path is somewhere in this string):\n{{ inputs.user_message | xml_escape | truncate(400) }}\n\nReply with a structured summary, max 1500 chars:\n  ## Target path\n  <resolved absolute path>\n\n  ## Modules (top-level)\n  - <module\/>: <one-line description>\n\n  ## Dependencies\n  <module> → <module>\n  ...\n\n  ## Hotspots\n  - <file>: <commit count last 90d>\n\n  ## Public surface\n  - <ClassName>: <one-line role>\n"
                },
                "skill": "history-explorer"
            },
            {
                "id": "classify_kind",
                "kind": "llm_classify",
                "with": {
                    "task": "Based on this codebase scan, which diagram kind is most informative\nfor an architecture doc? Pick ONE of:\n  - class: data structures \/ OO hierarchy dominant\n  - sequence: cross-module call flows dominant\n  - component: module-level boxes + arrows dominant\n  - deploy: infra \/ process layout dominant\n  - flow: data pipeline \/ staged processing dominant\n\nScan output:\n{{ outputs.scan_repo | truncate(1500) }}\n"
                },
                "depends_on": [
                    "scan_repo"
                ],
                "output_choices": [
                    "class",
                    "sequence",
                    "component",
                    "deploy",
                    "flow"
                ]
            },
            {
                "id": "render_plantuml",
                "kind": "agent",
                "with": {
                    "task": "Generate a PlantUML diagram source of kind `{{ outputs.classify_kind }}`\nfrom this codebase scan. Use idiomatic PlantUML syntax bracketed by\n`@startuml` ... `@enduml`. Aim for 10-20 boxes\/arrows; do not over-render.\n\nScan output:\n---\n{{ outputs.scan_repo | truncate(2000) }}\n---\n\nWrite the source to: `{{ inputs.workspace_dir }}\/diagrams\/arch.puml`\n(create parent dir if missing; overwrite OK).\n\nReply with the absolute output path on a single line, no preamble.\n"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "scan_repo",
                    "classify_kind"
                ]
            },
            {
                "id": "render_drawio",
                "kind": "agent",
                "with": {
                    "task": "Generate a draw.io XML diagram of kind `{{ outputs.classify_kind }}`\nfrom this codebase scan. Use valid draw.io XML:\n  <mxfile><diagram><mxGraphModel><root>\n    <mxCell id=\"0\"\/><mxCell id=\"1\" parent=\"0\"\/>\n    <mxCell id=\"N\" value=\"...\" style=\"...\" vertex=\"1\" parent=\"1\">\n      <mxGeometry ...\/>\n    <\/mxCell>\n    <mxCell ... edge=\"1\" source=\"...\" target=\"...\" parent=\"1\">\n      <mxGeometry ...\/>\n    <\/mxCell>\n  <\/root><\/mxGraphModel><\/diagram><\/mxfile>\n\nAim for 10-20 boxes\/edges to mirror the PlantUML side; layout can be\nsimple grid since the user is expected to re-arrange in draw.io.\n\nScan output:\n---\n{{ outputs.scan_repo | truncate(2000) }}\n---\n\nWrite the XML to: `{{ inputs.workspace_dir }}\/diagrams\/arch.drawio`\n(create parent dir if missing; overwrite OK).\n\nReply with the absolute output path on a single line, no preamble.\n"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "scan_repo",
                    "classify_kind"
                ]
            },
            {
                "id": "compose_doc",
                "kind": "agent",
                "with": {
                    "task": "Compose an architecture document that references both diagram\ndeliverables. The doc body should contain three sections:\n\n1. **Scope** — restate the user's invocation target.\n   User invocation: {{ inputs.user_message | xml_escape | truncate(200) }}\n2. **Diagram kind** — `{{ outputs.classify_kind }}` (one paragraph\n   justifying why this kind was chosen).\n3. **Deliverables** — two bullet items linking the files:\n   - PlantUML source: {{ outputs.render_plantuml }}\n   - draw.io file: {{ outputs.render_drawio }}\n\nSave to: `{{ inputs.workspace_dir }}\/diagrams\/arch.docx`. Reply\nwith the absolute output path on a single line, no preamble."
                },
                "skill": "docx",
                "depends_on": [
                    "render_plantuml",
                    "render_drawio"
                ]
            },
            {
                "id": "persist",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/architecture-snapshots.md",
                    "content": "=== diagram triangulation ===\ninvocation: {{ inputs.user_message | xml_escape | truncate(200) }}\ndiagram_kind: {{ outputs.classify_kind }}\nplantuml: {{ outputs.render_plantuml | truncate(200) }}\ndrawio: {{ outputs.render_drawio | truncate(200) }}\ndocx: {{ outputs.compose_doc | truncate(200) }}"
                },
                "depends_on": [
                    "compose_doc"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Scan a target codebase path, classify the most informative diagram kind, then render it as BOTH a PlantUML source file AND a draw.io XML in parallel, and compose them into a single architecture doc. Use when writing an RFC or onboarding doc and you want a text-friendly (PlantUML) and an editable (drawio) view of the same architecture.",
    "meta_priority": 55
}

{
    "kind": "meta",
    "name": "meta-github-pr-watch-digest",
    "always": false,
    "triggers": [
        "PR 巡检",
        "PR digest",
        "github 巡检",
        "watch my prs"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "pull_prs",
                "with": {
                    "task": "List open PRs in the relevant repos: those awaiting my review, those I authored that received new comments, and any failing CI runs. Filter scope from this user prompt: {{ inputs.user_message | xml_escape | truncate(256) }}"
                },
                "skill": "github"
            },
            {
                "id": "digest",
                "with": {
                    "text": "{{ outputs.pull_prs }}",
                    "style": "bulleted",
                    "max_words": 400
                },
                "skill": "summarize",
                "depends_on": [
                    "pull_prs"
                ]
            },
            {
                "id": "memorize",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/pr-watch.md",
                    "content": "{{ outputs.digest }}"
                },
                "depends_on": [
                    "digest"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Inspect the user's open GitHub PRs \/ failing CI \/ new issues via `gh`, summarize into 3 buckets (to-review \/ awaiting-me \/ CI-red), and persist follow-ups to memory.",
    "meta_priority": 45
}

{
    "kind": "meta",
    "name": "meta-issue-to-pr-autopilot",
    "always": false,
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "vcs",
                "filesystem-write",
                "network-write",
                "subprocess"
            ]
        }
    },
    "triggers": [],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "fetch_issue",
                "with": {
                    "task": "Fetch the issue referenced in the user request and gather the relevant repo context: {{ inputs.user_message | xml_escape | truncate(512) }}"
                },
                "skill": "github"
            },
            {
                "id": "patch",
                "with": {
                    "task": "Implement a fix for this issue.",
                    "issue": "{{ outputs.fetch_issue }}"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "fetch_issue"
                ]
            },
            {
                "id": "pr_body",
                "with": {
                    "text": "{{ outputs.patch }}",
                    "style": "pr_description",
                    "max_words": 400
                },
                "skill": "summarize",
                "depends_on": [
                    "patch"
                ]
            },
            {
                "id": "open_pr",
                "with": {
                    "task": "Open a pull request with the fix. Title: fix: {{ inputs.user_message | xml_escape | truncate(80) }}. Body: {{ outputs.pr_body }}"
                },
                "skill": "github",
                "depends_on": [
                    "pr_body"
                ]
            }
        ]
    },
    "description": "[DEPRECATED] Issue-to-PR autopilot — opens a PR via `gh`, runs a sub-agent fix loop, and writes to git. Disabled pending the E5 bounded sub-agent contract + side-effect ledger (plan §3.1 A8 \/ §5.3 E4): no risk metadata enforcement, no per-step budget, no rollback path. Do not re-enable without `metadata.opensquilla.risk: high` + capabilities {vcs, filesystem-write, network-write, subprocess} and a saga-style compensation step.",
    "meta_priority": 0,
    "disable-model-invocation": true
}

{
    "kind": "meta",
    "name": "meta-knowledge-base-bootstrap",
    "always": false,
    "triggers": [
        "搭建知识库",
        "knowledge base",
        "kb 启动",
        "bootstrap kb"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "classify",
                "kind": "llm_classify",
                "with": {
                    "text": "Inspect this user input and decide its primary source type.\n\nDecision rules:\n- URL  → input contains an http\/https link to a webpage (not a PDF, not a git host).\n- PDF  → input references a .pdf path or URL ending in .pdf.\n- GIT  → input references github.com \/ gitlab \/ a .git URL \/ a local repo path.\n- TEXT → everything else (free-text topic, question, concept).\n\nInput:\n{{ inputs.user_message | xml_escape | truncate(400) }}\n"
                },
                "output_choices": [
                    "URL",
                    "PDF",
                    "GIT",
                    "TEXT"
                ]
            },
            {
                "id": "ingest",
                "kind": "skill_exec",
                "skill": "multi-search-engine",
                "depends_on": [
                    "classify"
                ]
            },
            {
                "id": "memorize",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/kb-bootstrap.md",
                    "content": "# KB Bootstrap: {{ inputs.user_message | xml_escape | truncate(80) }}\nClassifier verdict: {{ outputs.classify }}\nIngestion (multi-search-engine, JSON):\n{{ outputs.ingest | truncate(2000) }}\n"
                },
                "depends_on": [
                    "ingest"
                ]
            },
            {
                "id": "index",
                "with": {
                    "task": "Create a workbook 'kb-index.xlsx' with columns [Engine, Title, URL, Snippet]. Populate it from this multi-search-engine JSON output: {{ outputs.ingest | truncate(3000) }}"
                },
                "skill": "xlsx",
                "depends_on": [
                    "ingest"
                ]
            }
        ]
    },
    "description": "Bootstrap a domain knowledge base from a single seed (URL \/ PDF path \/ git repo \/ free-text topic): classify source → ingest with the right tool → persist to memory + xlsx index.",
    "meta_priority": 40
}

{
    "kind": "meta",
    "name": "meta-migration-assistant",
    "always": false,
    "triggers": [
        "migration plan",
        "migration checklist",
        "practical migration checklist",
        "migrate from",
        "migrate",
        "upgrade from",
        "CommonJS to native ESM",
        "CommonJS to ESM",
        "CJS to ESM",
        "native ESM",
        "rollout risks",
        "升级指南",
        "迁移方案",
        "迁移步骤"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "migration_intake",
                "kind": "llm_chat",
                "with": {
                    "task": "Extract the requested migration source, target, version context, and\nrepository scope from the user request. Set NEEDS_CLARIFICATION: yes\nonly when the source or target stack is absent or the request is too\ngeneric to classify safely.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1400) }}\n\nReturn exactly:\nSOURCE_STACK: <source stack\/version, or MISSING_SOURCE_STACK>\nTARGET_STACK: <target stack\/version, or MISSING_TARGET_STACK>\nVERSION_CONTEXT: <version\/runtime\/package context, or unknown>\nREPO_SCOPE: <current diff|current branch|named repo|not specified>\nNEEDS_CLARIFICATION: <yes|no>\nMISSING_FIELDS:\n  - <source_stack|target_stack|none>\nCLARIFY_REASON: <one concise reason, or none>\n",
                    "system": "You extract migration request boundaries and decide whether clarification is required."
                }
            },
            {
                "id": "migration_clarify",
                "kind": "user_input",
                "when": "'NEEDS_CLARIFICATION: yes' in outputs.migration_intake",
                "clarify": {
                    "mode": "form",
                    "intro": "迁移目标还不够明确。请补齐源技术栈和目标技术栈，我再生成可执行迁移清单。\n",
                    "fields": [
                        {
                            "name": "source_stack",
                            "type": "string",
                            "prompt": "源技术栈\/版本 \/ Source stack or version",
                            "required": true,
                            "max_chars": 160
                        },
                        {
                            "name": "target_stack",
                            "type": "string",
                            "prompt": "目标技术栈\/版本 \/ Target stack or version",
                            "required": true,
                            "max_chars": 160
                        },
                        {
                            "name": "version_context",
                            "type": "string",
                            "prompt": "运行时、框架或包版本 \/ Runtime, framework, or package versions",
                            "max_chars": 240
                        },
                        {
                            "name": "repo_scope",
                            "type": "string",
                            "prompt": "仓库范围 \/ Repository scope",
                            "max_chars": 200
                        }
                    ],
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "算了",
                        "取消",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "migration_intake"
                ]
            },
            {
                "id": "classify",
                "kind": "llm_classify",
                "with": {
                    "text": "User said: {{ inputs.user_message | xml_escape | truncate(1400) }}\n\nMigration intake:\n{{ outputs.migration_intake | truncate(1200) }}\n\nClarification answers (may be empty when not needed):\n{{ inputs.get('collected', {}).get('migration_clarify', {}) | tojson }}\n\nIdentify the migration kind.\nIgnore benchmark wrappers, timestamps, locale hints, and generic\n\"return inline\" instructions. The actual user request may appear\nafter benchmark constraints; classify from the explicit source and\ntarget migration words, not from the preamble.\n\nDecision rules:\n- PY2_TO_PY3        → mentions Python 2 → 3 \/ py2 → py3\n- VUE2_TO_VUE3      → mentions Vue 2 → Vue 3 \/ options → composition\n- REACT_CLASS_TO_HOOKS → React class component → hooks\n- OPENAI_V0_TO_V1   → openai SDK v0 → v1, ChatCompletion → chat.completions.create\n- CJS_TO_ESM        → any mention of CommonJS, CJS, native ESM,\n  require(), module.exports, exports.*, import\/export migration,\n  \"from CommonJS to native ESM\", or \"CJS to ESM\"\n- OTHER             → any other migration request\n\nIf the text contains \"CommonJS\" and \"native ESM\", return exactly\nCJS_TO_ESM even if other benchmark\/context words appear first.\n"
                },
                "depends_on": [
                    "migration_intake",
                    "migration_clarify"
                ],
                "output_choices": [
                    "PY2_TO_PY3",
                    "VUE2_TO_VUE3",
                    "REACT_CLASS_TO_HOOKS",
                    "OPENAI_V0_TO_V1",
                    "CJS_TO_ESM",
                    "OTHER"
                ]
            },
            {
                "id": "fetch_guide",
                "kind": "skill_exec",
                "with": {
                    "query": "Authoritative migration guide for the user's actual migration.\nClassifier verdict: {{ outputs.classify }}.\nMigration intake:\n{{ outputs.migration_intake | truncate(1000) }}\nClarification answers:\n{{ inputs.get('collected', {}).get('migration_clarify', {}) | tojson }}\nIf the request mentions CommonJS, CJS, native ESM, require(),\nmodule.exports, or import\/export migration, search specifically for\ncurrent CommonJS to native ES Modules migration guidance covering\npackage.json type\/exports, extension rules, directory imports,\nJSON imports\/import attributes, createRequire interop, TypeScript\nNodeNext, test runners, dual publish, and consumer compatibility.\nIgnore benchmark preambles, timestamps, and unrelated local context.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(500) }}\n",
                    "engines": [
                        "duckduckgo",
                        "brave"
                    ],
                    "max_results": 8
                },
                "skill": "multi-search-engine",
                "depends_on": [
                    "classify",
                    "migration_clarify"
                ]
            },
            {
                "id": "repo_context",
                "kind": "skill_exec",
                "when": "(\n  'current diff' in (inputs.user_message | lower)\n  or 'this diff' in (inputs.user_message | lower)\n  or 'current branch' in (inputs.user_message | lower)\n  or 'pull request' in (inputs.user_message | lower)\n  or 'merge request' in (inputs.user_message | lower)\n  or 'already changed' in (inputs.user_message | lower)\n  or 'worktree' in (inputs.user_message | lower)\n  or '当前 diff' in inputs.user_message\n  or '当前分支' in inputs.user_message\n)\n",
                "with": {
                    "mode": "cached_fallback_worktree"
                },
                "skill": "git-diff",
                "depends_on": [
                    "classify"
                ]
            },
            {
                "id": "write_plan",
                "kind": "llm_chat",
                "with": {
                    "task": "Migration kind: {{ outputs.classify }}\nMigration intake:\n{{ outputs.migration_intake | truncate(1200) }}\nClarification answers:\n{{ inputs.get('collected', {}).get('migration_clarify', {}) | tojson }}\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nHard override before writing:\n- If the user request contains CommonJS, CJS, native ESM,\n  require\/module.exports, or import\/export migration wording, set\n  EFFECTIVE_KIND=CJS_TO_ESM.\n- If EFFECTIVE_KIND=CJS_TO_ESM, the Summary heading and every\n  section must be about CommonJS to native ES Modules. Do not write\n  about timezone, cloud, Python, Vue, React hooks, OpenAI SDK, or\n  any unrelated migration.\n- If classifier output conflicts with explicit source\/target words\n  in the user request, ignore the classifier output.\n\nAuthoritative guide excerpt:\n{{ outputs.fetch_guide | truncate(2000) }}\n\nOptional repository diff context:\n{{ outputs.repo_context | truncate(3000) }}\n\nTask lock:\n- The user's requested migration kind is authoritative. Do not let\n  repository diff context or unrelated local changes redefine the\n  migration. If the user asks for CommonJS\/CJS to native ESM, the\n  final answer must be a CommonJS-to-ESM migration checklist.\n- Apply this final-layer classifier override: if the user request\n  contains CommonJS, CJS, native ESM, require\/module.exports, or\n  import\/export migration wording, treat the effective migration\n  kind as CJS_TO_ESM even if `outputs.classify` says OTHER.\n- Do not expose classifier labels such as OTHER, CJS_TO_ESM,\n  VUE2_TO_VUE3, or internal routing decisions in the final answer.\n- Repository context is optional and only evidentiary. If it does\n  not explicitly show files relevant to the classified migration,\n  ignore it and say repository files were not verified.\n- Do not invent repo-specific files, package names, scripts, or\n  changes. Use grep commands for discovery instead.\n- Do not include write\/commit commands such as `git add`,\n  `git commit`, `eslint --fix`, or codemods inside validation\n  sections. Validation commands are read-only.\n- Benchmark\/no-write constraint: the final answer must not include\n  file creation or mutation commands in validation or smoke-test\n  sections. Forbidden examples include heredocs, shell redirection\n  (`>`, `>>`), `cat >`, `tee`, `touch`, `mkdir`, `cp`, `mv`,\n  `rm`, `npm init`, config generators, codemods, and `python -c`\n  \/ `node -e` snippets that write files. If a smoke test normally\n  needs a temporary fixture, describe it as a manual implementation\n  step, not as a validation command.\n- Do not use unverified concrete entrypoint paths such as\n  `index.js`, `server.js`, or package-specific test commands unless\n  they came from repository evidence. Use discovery commands first\n  and phrase path-specific commands as templates.\n- If the authoritative guide excerpt is about a different migration\n  domain, ignore it completely. Do not mention that it was ignored.\n- Do not wrap the whole response in ```markdown fences.\n\nProduce a concrete migration checklist as Markdown with these sections:\n## Summary\n## Evidence boundary\n## Breaking changes\n## Step-by-step\n## Repository discovery checklist\n## Files likely affected (grep patterns the user can run)\n## Validation (tests\/checks to confirm the migration)\n## Rollout and rollback\n\nQuality contract:\n- Keep the answer dense and complete in one response. Target\n  1,200-1,800 words; prefer compact tables and bullets over long\n  explanations or large config snippets.\n- If repository context is empty, unreadable, or generic, say so\n  clearly; do not invent files or package names.\n- If repository context is present but about another migration,\n  ignore it and explicitly state that it is unrelated to the user's\n  requested migration.\n- Prefer `rg` commands for grep patterns and make them copyable.\n- For CJS_TO_ESM discovery, include these command families:\n  `node -v`, `npm pkg get type main exports scripts`,\n  `git grep -nE \"require\\(|module\\.exports|exports\\.\"`,\n  `git grep -nE \"require\\([^'\\\"\\`]+\\)\"`,\n  `git grep -nE \"from ['\\\"]\\.\/[^'\\\"]+['\\\"]|import\\(['\\\"]\\.\/[^'\\\"]+['\\\"]\\)\"`,\n  `git grep -nE \"\\b(__dirname|__filename)\\b\"`,\n  `git grep -nE \"require\\.(resolve|cache|main|extensions)\"`,\n  `git grep -nE \"require\\(['\\\"][^'\\\"]+\\.json['\\\"]\\)\"`,\n  `git ls-files '*.cjs' '*.mjs'`, and\n  `git ls-files '**\/package.json' | xargs grep -l '\"main\"\\|\"type\"\\|\"exports\"'`.\n- Validation should be hypothesis-driven: include separate checks\n  for happy path package load, dynamic require replacement,\n  extension\/index-resolution failures, JSON import behavior,\n  test-runner ESM behavior, and downstream consumer import\/require\n  smoke tests.\n- Express validation commands as read-only probes against existing\n  repository files. For example, use `node --check <existing-file>`,\n  `node --input-type=module -e \"import('package-name-or-path')\"`,\n  `npm test -- --runInBand` only when scripts exist, and\n  `git grep`\/`npm pkg get` discovery before any path-specific\n  command. Never ask the user to create `tmp-smoke.*` files.\n- Include package\/publication validation commands when relevant:\n  `npm pack --dry-run`, `npx publint`, and\n  `npx @arethetypeswrong\/cli --pack .` for published packages with\n  TypeScript types. Label these as optional if the package is not\n  published.\n- For rollout, include decision points: ESM-only versus dual publish,\n  semver-major trigger, consumer compatibility scan, canary\/internal\n  package release, package-tarball inspection, rollback to previous\n  package version, and keeping `.cjs` escape hatches for tooling.\n- For CJS_TO_ESM, cover at minimum: `type: module`, extension rules,\n  directory `index.js` imports, `__dirname`\/`__filename`, JSON\n  imports, CJS interop\/default export shape changes, `exports` map\n  subpath whitelisting and precedence, TypeScript `NodeNext`,\n  Jest\/Vitest or node:test, dual-package hazards, downstream\n  consumer smoke tests, release sequencing, and rollback.\n- Do not say that `exports` deprecates `main`; say that `exports` takes precedence for runtimes that support it and can restrict deep imports.\n- Mark all validation commands as commands for the user to run; do\n  not imply they were executed.\n- Include version caveats for Node\/tooling-specific behavior.\n- For JSON imports, avoid claiming one universal syntax. Say that\n  behavior differs by Node version\/tooling; verify the target\n  runtime's current JSON-module\/import-attributes support, and keep\n  `createRequire(import.meta.url)` as the conservative fallback when\n  needed.\n- Avoid invented loader placeholders such as\n  `node --loader <your-loader>` unless the repository evidence\n  shows an existing loader. Prefer concrete discovery commands.\n- Avoid write-implying validation examples such as `eslint --fix`;\n  validation should be read-only unless clearly labeled as a\n  separate codemod\/fix step.\n- Avoid file-creation or config-generation examples unless they are\n  explicitly labeled as implementation work, not validation. Keep\n  the checklist concise and decision-oriented instead of filling it\n  with large generic config snippets.\n- Do not include brittle placeholder commands such as\n  `node --loader <your-loader>` or `node --check <existing-file>`;\n  write discovery-first templates with clear placeholders only where\n  unavoidable.\n- Avoid obsolete Node flags such as `--experimental-modules`; prefer\n  current LTS-compatible commands and call out when a command is\n  version-specific.",
                    "system": "You write migration checklists. Answer the user's requested\nmigration only. Do not change the migration domain based on locale,\ntimezone, unrelated repository diffs, or general environment\ncontext. If the request mentions CommonJS, CJS, native ESM,\nrequire\/module.exports, or import\/export migration, the answer must\nbe exclusively about CommonJS to native ESM. Return Markdown directly;\ndo not wrap the entire answer in a fenced code block.\n"
                },
                "depends_on": [
                    "classify",
                    "migration_clarify",
                    "fetch_guide",
                    "repo_context"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when the user needs a concrete migration plan that benefits from multi-skill orchestration across migration classification, authoritative guide lookup, optional repo diff inspection, and step-by-step validation planning.",
    "meta_priority": 50,
    "final_text_mode": "step:write_plan"
}

{
    "kind": "meta",
    "name": "meta-multi-format-export-pack",
    "always": false,
    "triggers": [
        "多格式导出",
        "multi format export",
        "全格式输出",
        "export pack"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "model",
                "with": {
                    "text": "{{ inputs.user_message | xml_escape | truncate(40000) }}",
                    "style": "structured_model",
                    "max_words": 1500
                },
                "skill": "summarize"
            },
            {
                "id": "to_docx",
                "with": {
                    "body": "{{ outputs.model }}",
                    "title": "{{ inputs.user_message | xml_escape | truncate(128) }} — report"
                },
                "skill": "docx",
                "depends_on": [
                    "model"
                ]
            },
            {
                "id": "to_pptx",
                "with": {
                    "title": "{{ inputs.user_message | xml_escape | truncate(128) }} — slides",
                    "outline": "{{ outputs.model }}"
                },
                "skill": "pptx",
                "depends_on": [
                    "model"
                ]
            },
            {
                "id": "to_xlsx",
                "with": {
                    "task": "Create a workbook named '{{ inputs.user_message | slugify | truncate(64) }}-data.xlsx' from this structured content: {{ outputs.model }}"
                },
                "skill": "xlsx",
                "depends_on": [
                    "model"
                ]
            },
            {
                "id": "to_pdf",
                "with": {
                    "html": "<!DOCTYPE html>\n<html><head><meta charset=\"utf-8\"><title>{{ inputs.user_message | xml_escape | truncate(128) }}<\/title><\/head>\n<body>\n  <h1>{{ inputs.user_message | xml_escape | truncate(128) }}<\/h1>\n  <article>{{ outputs.model | xml_escape }}<\/article>\n<\/body><\/html>\n",
                    "page_size": "A4"
                },
                "skill": "html-to-pdf",
                "depends_on": [
                    "model"
                ]
            }
        ]
    },
    "description": "From one piece of source content, render four deliverables: .docx report, .pptx slides, .xlsx data, and an HTML\/PDF public version.",
    "meta_priority": 60
}

{
    "kind": "meta",
    "name": "meta-pdf-intelligence",
    "always": false,
    "triggers": [
        "看一下这个 PDF",
        "看看这个 PDF",
        "读一下这个 PDF",
        "分析这个 PDF",
        "总结这个 PDF",
        "帮我看 PDF",
        "处理 PDF",
        "PDF 抽要",
        "PDF intelligence",
        "pdf digest",
        "compare these PDFs",
        "page-backed findings",
        "PDF excerpt",
        "pasted PDF excerpt",
        "PDF page says",
        "analyze these PDFs",
        "PDF analysis",
        "PDF comparison",
        "PDF 摘录"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "intake",
                "kind": "llm_chat",
                "with": {
                    "task": "Parse the PDF request into a document-analysis contract. Determine\nwhether this is a single-PDF summary, multi-PDF comparison, or a\ntargeted question-answer task. Preserve every file path or URL the\nuser mentioned. Treat quoted page text, pasted excerpts, and phrases\nlike \"I don't have the PDF upload handy\" as first-class source\nstatus signals.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nReturn exactly:\nMODE: <single_summary|multi_compare|question_answer>\nSOURCE_STATUS: <readable_pdf|inline_excerpts_only|mixed_pdf_and_inline|reference_without_content>\nDOCUMENTS:\n  - <path or URL>\nUSER_EXCERPTS:\n  - PAGE: <page number or unknown>\n    QUOTE: <verbatim user-provided excerpt or empty>\nQUESTION: <specific question or empty>\nOUTPUT_LANGUAGE: <language>\nNEEDS_CLARIFICATION: <yes|no>\nMISSING_FIELDS:\n  - <source_material|question|none>\nCLARIFY_REASON: <one concise reason, or none>\n",
                    "system": "You parse PDF-analysis requests into strict extraction contracts."
                }
            },
            {
                "id": "pdf_clarify",
                "kind": "user_input",
                "when": "'NEEDS_CLARIFICATION: yes' in outputs.intake",
                "clarify": {
                    "mode": "form",
                    "intro": "PDF 分析缺少可用来源或目标问题。请补齐材料，或确认只基于已提供摘录\/文件名给出有限结论。\n",
                    "fields": [
                        {
                            "name": "source_status",
                            "type": "enum",
                            "prompt": "来源状态 \/ Source status",
                            "choices": [
                                "readable_pdf",
                                "inline_excerpts_only",
                                "reference_only"
                            ],
                            "required": true
                        },
                        {
                            "name": "source_material",
                            "type": "string",
                            "prompt": "PDF 路径\/URL、上传说明，或页面摘录 \/ PDF path, URL, upload note, or excerpts",
                            "required": true,
                            "max_chars": 2000
                        },
                        {
                            "name": "question",
                            "type": "string",
                            "prompt": "具体问题 \/ Specific question",
                            "max_chars": 300
                        },
                        {
                            "name": "output_language",
                            "type": "enum",
                            "prompt": "输出语言 \/ Output language",
                            "choices": [
                                "zh",
                                "en",
                                "ja",
                                "other"
                            ],
                            "default": "zh"
                        }
                    ],
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "算了",
                        "取消",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "intake"
                ]
            },
            {
                "id": "extract",
                "when": "'SOURCE_STATUS: inline_excerpts_only' not in outputs.intake and (\n  'SOURCE_STATUS: reference_without_content' not in outputs.intake\n  or inputs.get('collected', {}).get('pdf_clarify', {}).get('source_status') == 'readable_pdf'\n) and \"don't have the pdf\" not in (inputs.user_message | lower) and \"do not have the pdf\" not in (inputs.user_message | lower) and \"no pdf upload\" not in (inputs.user_message | lower) and \"pdf upload handy\" not in (inputs.user_message | lower) and not ('page ' in (inputs.user_message | lower) and ' says ' in (inputs.user_message | lower))",
                "with": {
                    "task": "Extract text, tables, page numbers, headings, and document names for\nthis PDF analysis contract:\n{{ outputs.intake | truncate(2000) }}\n\nDo not invent PDF content. If no readable local path, URL, or\nattachment is actually accessible, return UNAVAILABLE with the\nreason instead of a synthetic summary.\n"
                },
                "skill": "pdf-toolkit",
                "depends_on": [
                    "intake",
                    "pdf_clarify"
                ],
                "on_failure": "inline_excerpt_extract"
            },
            {
                "id": "inline_excerpt_extract",
                "kind": "llm_chat",
                "with": {
                    "task": "The PDF extraction skill could not read the file in this runtime.\nBuild a minimal evidence packet only from filenames, URLs, quoted\nexcerpts, pasted text, and explicit user claims in the request.\nClearly label missing page evidence as unavailable.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(4000) }}\n",
                    "system": "You provide a safe fallback when a PDF file is unavailable to the extractor."
                }
            },
            {
                "id": "per_document_digest",
                "with": {
                    "text": "Intake:\n{{ outputs.intake }}\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(4000) }}\n\nExtracted PDF content:\n{{ outputs.extract }}\n\nIf extraction was skipped, unavailable, or inconsistent with the\nUSER_EXCERPTS in intake, summarize only the user-provided excerpts\nand explicitly mark all other document content as unavailable.\n",
                    "style": "pdf_per_document_digest",
                    "max_words": 2500
                },
                "skill": "summarize",
                "depends_on": [
                    "extract"
                ]
            },
            {
                "id": "cross_document_synthesis",
                "kind": "llm_chat",
                "with": {
                    "task": "Synthesize the PDF analysis according to the intake mode. For\nsingle_summary, produce a structured summary. For multi_compare,\ncompare agreements, conflicts, and unique claims. For question_answer,\nanswer the question directly first.\n\nRequirements:\n- produce a compact final deliverable, not process commentary\n- source hierarchy: first trust verbatim user-provided excerpts and\n  pasted text; then trust extractor output only when it is actually\n  from a readable PDF and does not conflict with the user excerpts;\n  then place synthesis under Inferences\n- if SOURCE_STATUS is inline_excerpts_only or reference_without_content,\n  ignore any downstream claims that are not present in USER_EXCERPTS\n  or the original user request\n- if the original user request says the PDF is not uploaded, no PDF\n  is handy, or uses inline phrasing like \"page 3 says ...\", treat\n  the entire answer as EXCERPT-ONLY even if intake or a downstream\n  digest says otherwise\n- if extractor output conflicts with USER_EXCERPTS, treat it as an\n  extraction anomaly, do not include the conflicting claim as fact\n- in EXCERPT-ONLY mode, never claim page count, section headings,\n  tables, figures, authors, in-memory extraction, or unseen page\n  coverage unless those exact facts appear in the user's request\n- use Evidence IDs: E1, E2, E3...\n- include an Evidence Matrix with columns:\n  ID | Document | Page | Evidence | Supports | Confidence\n- cite file names and page numbers whenever available\n- every Key Fact must cite at least one Evidence ID\n- separate Direct Evidence from Inferences; do not put inference\n  inside the fact list\n- never merge evidence from different documents without naming them\n- if the PDF file was not available and only excerpts\/user claims\n  were provided, label the answer EXCERPT-ONLY and do not make\n  document-wide claims\n- for EXCERPT-ONLY answers, include a Source Excerpts table with\n  Page and Verbatim Text before key facts\n- include open questions, extraction limits, and verification needs\n- include a Reusable Memory Index as YAML or JSON with:\n  documents, evidence_ids, key_facts, page_refs, open_questions,\n  tags, confidence\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(4000) }}\n\nClarification answers (may be empty when not needed):\n{{ inputs.get('collected', {}).get('pdf_clarify', {}) | tojson }}\n\nIntake:\n{{ outputs.intake | truncate(2000) }}\n\nPer-document digest:\n{{ outputs.per_document_digest | truncate(8000) }}",
                    "system": "You synthesize PDF findings with traceable evidence, evidence IDs, and explicit limits."
                },
                "depends_on": [
                    "per_document_digest"
                ]
            },
            {
                "id": "traceable_index",
                "kind": "llm_chat",
                "with": {
                    "task": "Build a compact memory index for later recall. Use structured fields:\ndocuments, key_facts, page_refs, tables, open_questions.\n\nAnalysis:\n{{ outputs.cross_document_synthesis | truncate(6000) }}",
                    "system": "You build compact structured indexes for later PDF recall."
                },
                "depends_on": [
                    "cross_document_synthesis"
                ]
            },
            {
                "id": "memorize",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/pdf-intel.md",
                    "content": "{{ outputs.traceable_index }}"
                },
                "depends_on": [
                    "traceable_index"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when the user needs PDF analysis, pasted PDF excerpt analysis, digesting, comparison, or question answering that benefits from multi-skill orchestration across PDF extraction, summarization, cross-document synthesis, traceable evidence indexing, and memory capture.",
    "meta_priority": 55,
    "final_text_mode": "step:cross_document_synthesis"
}

{
    "kind": "meta",
    "name": "meta-pdf-reformat-pipeline",
    "always": false,
    "triggers": [
        "pdf 重排",
        "pdf reformat",
        "pdf 现代化",
        "rewrite pdf"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "extract",
                "with": {
                    "task": "Extract structured text + page metadata from the PDF referenced in: {{ inputs.user_message | xml_escape | truncate(512) }}"
                },
                "skill": "pdf-toolkit"
            },
            {
                "id": "rewrite",
                "with": {
                    "instruction": "Modernize phrasing and standardize headings on problematic pages while preserving original meaning.",
                    "source_text": "{{ outputs.extract }}"
                },
                "skill": "nano-pdf",
                "depends_on": [
                    "extract"
                ]
            },
            {
                "id": "audit",
                "with": {
                    "text": "{{ outputs.rewrite }}",
                    "style": "change_summary",
                    "max_words": 600
                },
                "skill": "summarize",
                "depends_on": [
                    "rewrite"
                ]
            },
            {
                "id": "merge",
                "with": {
                    "task": "Merge the rewritten pages back into the source PDF. Audit summary for review: {{ outputs.audit }}"
                },
                "skill": "pdf-toolkit",
                "depends_on": [
                    "rewrite",
                    "audit"
                ]
            }
        ]
    },
    "description": "Modernize a legacy PDF: structural extraction → natural-language rewrite of problem pages → audit summary → re-merge into the final PDF.",
    "meta_priority": 45
}

{
    "kind": "meta",
    "name": "meta-pre-commit-quality-gate",
    "always": false,
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "subprocess",
                "filesystem-read",
                "filesystem-write"
            ]
        }
    },
    "triggers": [
        "pre-commit quality gate",
        "pre-commit 质量门",
        "提交前质量检查"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "collect_staged",
                "kind": "skill_exec",
                "with": {
                    "cwd": "{{ inputs.workspace_dir | default('.') }}",
                    "mode": "staged_files"
                },
                "skill": "git-diff"
            },
            {
                "id": "run_ruff",
                "kind": "agent",
                "with": {
                    "task": "Run `uv run ruff check <staged .py files>` on the staged Python files\nlisted below. Filter for paths ending in `.py` only.\n\nStaged files:\n---\n{{ outputs.collect_staged | truncate(800) }}\n---\n\nIf the staged list is NO_STAGED_FILES or contains no .py files, treat\nas PASS.\n\nReply with EXACTLY one line, no preamble:\n  PASS: ruff clean\n  FAIL: <count> findings — <head of first 5 violation lines>\n"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "collect_staged"
                ]
            },
            {
                "id": "run_mypy",
                "kind": "agent",
                "with": {
                    "task": "Run `uv run mypy --show-error-codes` on the staged Python files that\nlive under `src\/opensquilla\/` (skip files outside that tree because\nthe project's mypy config only covers the package).\n\nStaged files:\n---\n{{ outputs.collect_staged | truncate(800) }}\n---\n\nIf no `src\/opensquilla\/**` files are staged, treat as PASS.\n\nReply with EXACTLY one line, no preamble:\n  PASS: mypy clean (or no src files staged)\n  FAIL: <count> errors — <head of first 3 errors>\n"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "collect_staged"
                ]
            },
            {
                "id": "run_pytest",
                "kind": "agent",
                "with": {
                    "task": "Identify pytest files affected by these staged files (any\n`tests\/test_*.py` that imports or references a same-named module).\nRun `uv run pytest -q -x --tb=short` on the affected subset; if you\ncannot localise, run the default-path suite with the same flags.\n\nStaged files:\n---\n{{ outputs.collect_staged | truncate(800) }}\n---\n\nReply with EXACTLY one line, no preamble:\n  PASS: <count> tests passed\n  FAIL: <first failing test name> — <one-line error>\n"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "collect_staged"
                ]
            },
            {
                "id": "arbitrate",
                "kind": "agent",
                "with": {
                    "task": "Three quality gates ran over the staged diff:\n  - ruff:   {{ outputs.run_ruff }}\n  - mypy:   {{ outputs.run_mypy }}\n  - pytest: {{ outputs.run_pytest }}\n\nApply the rule STRICTLY (do NOT soften):\n  * If ANY of the three starts with \"FAIL\" → final verdict is BLOCK.\n    The BLOCK summary concatenates each failing gate's verbatim text.\n  * If ALL three start with \"PASS\" → final verdict is APPROVE.\n\nReply with EXACTLY this structure on the first line, then optionally\nadditional lines:\n  BLOCK: <one-line summary; list every gate that failed>\n  APPROVE: ruff\/mypy\/pytest all green"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "run_ruff",
                    "run_mypy",
                    "run_pytest"
                ]
            },
            {
                "id": "persist",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/pre-commit-gates.md",
                    "content": "=== pre-commit quality gate ===\ninvocation: {{ inputs.user_message | xml_escape | truncate(200) }}\nstaged: {{ outputs.collect_staged | truncate(600) }}\nruff:   {{ outputs.run_ruff | truncate(200) }}\nmypy:   {{ outputs.run_mypy | truncate(200) }}\npytest: {{ outputs.run_pytest | truncate(200) }}\nverdict: {{ outputs.arbitrate | truncate(400) }}"
                },
                "depends_on": [
                    "arbitrate"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Run three quality gates (ruff + mypy + pytest) in parallel over the staged diff, then arbitrate a single BLOCK\/APPROVE verdict. Use before committing changes locally when you want a comprehensive pre-commit gate beyond per-file linting — exactly the same gate set CI enforces.",
    "meta_priority": 70
}

{
    "kind": "meta",
    "name": "meta-scheduled-morning-digest",
    "always": false,
    "triggers": [
        "晨报",
        "morning digest",
        "每日简报"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "weather",
                "with": {
                    "location": "{{ inputs.user_message | xml_escape | truncate(128) }}"
                },
                "skill": "weather"
            },
            {
                "id": "news",
                "with": {
                    "query": "{{ inputs.user_message | xml_escape | truncate(256) }} latest news",
                    "engines": [
                        "brave",
                        "duckduckgo"
                    ],
                    "max_results": 5
                },
                "skill": "multi-search-engine"
            },
            {
                "id": "digest",
                "with": {
                    "text": "Weather:\n{{ outputs.weather }}\n\nNews:\n{{ outputs.news }}",
                    "style": "bulleted",
                    "max_words": 500
                },
                "skill": "summarize",
                "depends_on": [
                    "weather",
                    "news"
                ]
            },
            {
                "id": "memorize",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/morning-digest.md",
                    "content": "{{ outputs.digest }}"
                },
                "depends_on": [
                    "digest"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Compose a morning digest combining local weather, news for the user's interest topic, a structured summary, and a memory note.",
    "meta_priority": 40
}

{
    "kind": "meta",
    "name": "meta-security-review-bundle",
    "always": false,
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "filesystem-read",
                "network-read",
                "subprocess"
            ]
        }
    },
    "triggers": [
        "security review",
        "安全审查",
        "审查这段代码",
        "review for safety"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "policy_review",
                "kind": "agent",
                "with": {
                    "task": "You are the *policy reviewer* for a security-review bundle.\nApply only the rules below; do not invent additional concerns.\n\nOperation under review:\n---\n{{ inputs.user_message | xml_escape | truncate(800) }}\n---\n\nRules (in priority order):\n  1. DENY if the operation would modify `\/etc\/`, `\/boot\/`, system\n     systemd units, `~\/.ssh\/authorized_keys`, or `\/etc\/sudoers`.\n  2. DENY if the operation would exfiltrate credentials\n     (read `.env`, `~\/.aws\/credentials`, `~\/.ssh\/id_*`, etc.)\n     to a remote destination.\n  3. DENY if the operation would disable or bypass sandbox \/\n     selinux \/ apparmor controls.\n  4. Otherwise ALLOW.\n\nReply with EXACTLY one line, no preamble:\n  DENY: <one-sentence reason>\n  ALLOW: ok\n"
                },
                "skill": "sub-agent"
            },
            {
                "id": "secret_scan",
                "kind": "agent",
                "with": {
                    "task": "You are the *secret scanner* for a security-review bundle. You\ndo NOT judge intent — only detect credential-shaped strings.\n\nInput:\n---\n{{ inputs.user_message | xml_escape | truncate(800) }}\n---\n\nLook for:\n  * API keys (sk-…, ghp_…, AKIA…, AIza…, …)\n  * JWT \/ OAuth bearer tokens\n  * Private keys (-----BEGIN … PRIVATE KEY-----)\n  * Database connection URIs with embedded passwords\n  * Plaintext passwords next to obvious labels (pwd=, password:)\n\nReply with EXACTLY one line, no preamble:\n  WARN: <count> <one-line summary of kinds detected>\n  CLEAR: no secrets found\n"
                },
                "skill": "sub-agent"
            },
            {
                "id": "arbitrate",
                "kind": "agent",
                "with": {
                    "task": "Three independent security gates ran on this operation:\n\n- policy_review: {{ outputs.policy_review }}\n- secret_scan:   {{ outputs.secret_scan }}\n\nApply the arbitration rule STRICTLY in this priority order\n(higher wins; do NOT mix or soften):\n\n  1. If policy_review begins with \"DENY\" → final verdict is DENY.\n     Pass through the policy reviewer's reason verbatim.\n  2. Else if secret_scan begins with \"WARN\" → final verdict is WARN.\n     Pass through the scanner's summary verbatim and require\n     explicit user acknowledgement before proceeding.\n  3. Else (policy_review ALLOW and secret_scan CLEAR) → ALLOW.\n\nReply with EXACTLY this structure on the first line, then\nadditional lines as needed:\n\n  DENY: <policy reason>\n  WARN: <scanner summary; user must confirm>\n  ALLOW: cleared by both gates"
                },
                "skill": "sub-agent",
                "depends_on": [
                    "policy_review",
                    "secret_scan"
                ]
            },
            {
                "id": "audit_emit",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/security-review.md",
                    "content": "=== security review audit ===\noperation: {{ inputs.user_message | xml_escape | truncate(400) }}\npolicy_review: {{ outputs.policy_review | truncate(200) }}\nsecret_scan: {{ outputs.secret_scan | truncate(200) }}\nverdict: {{ outputs.arbitrate | truncate(400) }}"
                },
                "depends_on": [
                    "arbitrate"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Compose three independent security gates over a candidate operation — policy\/governance review, secret\/credential scan, and audit-log emit — then arbitrate the verdicts with a strict priority rule (governance DENY > scanner WARN > ALLOW). Use when reviewing a proposed code change, script, or environment manipulation for safety.",
    "meta_priority": 75
}

{
    "kind": "meta",
    "name": "meta-spreadsheet-insight",
    "always": false,
    "triggers": [
        "spreadsheet insight",
        "excel 分析",
        "xlsx 洞察",
        "数据复盘"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "read",
                "with": {
                    "task": "Inspect the spreadsheet referenced in this user request and return all sheets' data: {{ inputs.user_message | xml_escape | truncate(512) }}"
                },
                "skill": "xlsx"
            },
            {
                "id": "analyze",
                "with": {
                    "text": "{{ outputs.read }}",
                    "style": "trend_analysis",
                    "max_words": 1000
                },
                "skill": "summarize",
                "depends_on": [
                    "read"
                ]
            },
            {
                "id": "writeback",
                "with": {
                    "task": "Append a new 'Insights' sheet to the workbook with the following analysis: {{ outputs.analyze }}"
                },
                "skill": "xlsx",
                "depends_on": [
                    "analyze"
                ]
            },
            {
                "id": "memorize",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/spreadsheet-kpi.md",
                    "content": "{{ outputs.analyze }}"
                },
                "depends_on": [
                    "analyze"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Turn an Excel workbook into business insight: structured read → trend\/anomaly summary → write back to a new 'Insights' sheet → persist KPIs to memory.",
    "meta_priority": 55
}

{
    "kind": "meta",
    "name": "meta-stack-trace-investigator",
    "always": false,
    "metadata": {
        "opensquilla": {
            "risk": "low",
            "capabilities": [
                "shell",
                "filesystem-write"
            ]
        }
    },
    "triggers": [
        "traceback",
        "stack trace",
        "runtime error",
        "failing log",
        "keyerror",
        "typeerror",
        "investigate stack trace",
        "trace investigator",
        "诊断 traceback",
        "调查 stack trace",
        "查 traceback"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "trace_collect",
                "kind": "llm_chat",
                "with": {
                    "task": "Extract a compact investigation brief from the original request.\nDo NOT ask the user to confirm language, expected behavior, or\nrecent changes when the stack trace is enough to infer a useful\ninvestigation direction. If a field is absent, write ASSUMED or\nunknown and continue.\n\nOriginal request:\n---\n{{ inputs.user_message | xml_escape | truncate(3000) }}\n---\n\nReturn exactly this structure:\nLANGUAGE: <python|javascript|typescript|go|rust|unknown>\nEXPECTED_BEHAVIOR: <brief or ASSUMED: not provided>\nRECENT_CHANGES: <brief or ASSUMED: not provided>\nTRACE_PRESENT: <yes|no>\nPRIMARY_EXCEPTION: <exception\/error head or unknown>\nPRIMARY_FILES:\n  - <path:line if present, otherwise unknown>\n",
                    "system": "You extract stack-trace investigation facts without asking follow-up questions."
                }
            },
            {
                "id": "parse_trace",
                "kind": "llm_chat",
                "with": {
                    "task": "You are the trace parser for a stack-trace investigation bundle.\nExtract structured info from the stack trace below; do not speculate\nabout root cause yet.\n\nExtracted investigation brief (treat as hints, not authoritative):\n{{ outputs.trace_collect | xml_escape | truncate(1000) }}\n\nTraceback under investigation:\n---\n{{ inputs.user_message | xml_escape | truncate(3000) }}\n---\n\nReply with EXACTLY one JSON object on a single line, no preamble:\n  {\"exception_class\": \"<ClassNameOrErrorKind>\", \"exception_message\": \"<head of message; <=120 chars>\", \"primary_file\": \"<path\/file or empty>\", \"primary_line\": <int or 0>, \"symbols\": [\"sym1\", \"sym2\", ...], \"language\": \"<python|javascript|typescript|go|rust|unknown>\"}\n\nThe \"symbols\" list contains the function\/method names that appear in\nthe top 3 frames; include at most 6 distinct entries.\n",
                    "system": "You parse stack traces. Return only the requested JSON object."
                },
                "depends_on": [
                    "trace_collect"
                ]
            },
            {
                "id": "grep_repo",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "command": "rg -n --hidden --max-count 5 -- 'parse_tool_result|run_step|json.loads|KeyError|result' .",
                    "timeout": 12,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "parse_trace"
                ],
                "on_failure": "grep_repo_degraded",
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "grep_repo_degraded",
                "kind": "llm_chat",
                "with": {
                    "task": "Return exactly:\nREPO_GREP: DEGRADED - repository search could not run in this\nworkspace. Continue from traceback semantics and provide exact\ncommands for the target repository.\n",
                    "system": "You return a fixed degraded-evidence marker."
                }
            },
            {
                "id": "search_issues",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "command": "gh issue list --search 'KeyError result parse_tool_result' --json number,title,url --limit 10",
                    "timeout": 12,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "parse_trace"
                ],
                "on_failure": "search_issues_degraded",
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "search_issues_degraded",
                "kind": "llm_chat",
                "with": {
                    "task": "Return exactly:\nISSUE_SEARCH: DEGRADED - issue search could not run or produced no\nauthenticated results. Continue without issue evidence.\n",
                    "system": "You return a fixed degraded-evidence marker."
                }
            },
            {
                "id": "git_history",
                "kind": "tool_call",
                "tool": "exec_command",
                "tool_args": {
                    "command": "git log --since='30 days ago' --oneline -- src\/agent\/tools.py src\/agent\/runtime.py",
                    "timeout": 12,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "parse_trace"
                ],
                "on_failure": "git_history_degraded",
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "git_history_degraded",
                "kind": "llm_chat",
                "with": {
                    "task": "Return exactly:\nGIT_HISTORY: DEGRADED - git history could not run in this\nworkspace. Continue without commit evidence and provide exact git\nlog\/blame commands for the target repository.\n",
                    "system": "You return a fixed degraded-evidence marker."
                }
            },
            {
                "id": "diff_context",
                "kind": "skill_exec",
                "with": {
                    "cwd": "{{ inputs.workspace_dir | default('.') }}",
                    "mode": "worktree"
                },
                "skill": "git-diff",
                "depends_on": [
                    "parse_trace"
                ],
                "on_failure": "diff_context_degraded"
            },
            {
                "id": "diff_context_degraded",
                "kind": "llm_chat",
                "with": {
                    "task": "Return exactly:\nDIFF_CONTEXT: DEGRADED - current workspace is not a readable git\nworktree or git-diff failed. Continue using traceback evidence,\nrepo grep output, and explicit user-provided paths.\n",
                    "system": "You return a fixed degraded-evidence marker."
                }
            },
            {
                "id": "history_patterns",
                "kind": "skill_exec",
                "with": {
                    "query": "{{ outputs.parse_trace | truncate(512) }}",
                    "include": "meta_usage,co_occurrences",
                    "window_days": "30"
                },
                "skill": "history-explorer",
                "depends_on": [
                    "parse_trace"
                ],
                "on_failure": "history_patterns_degraded"
            },
            {
                "id": "history_patterns_degraded",
                "kind": "llm_chat",
                "with": {
                    "task": "Return exactly:\nHISTORY_PATTERNS: DEGRADED - history-explorer failed or no local\ndecision history is available. Continue without prior-pattern\nevidence.\n",
                    "system": "You return a fixed degraded-evidence marker."
                }
            },
            {
                "id": "memory_recall",
                "kind": "tool_call",
                "tool": "memory_search",
                "tool_args": {
                    "query": "{{ outputs.parse_trace | truncate(400) }}",
                    "max_results": 3
                },
                "depends_on": [
                    "parse_trace"
                ],
                "on_failure": "memory_recall_degraded",
                "tool_allowlist": [
                    "memory_search"
                ]
            },
            {
                "id": "memory_recall_degraded",
                "kind": "llm_chat",
                "with": {
                    "task": "Return exactly:\nMEMORY_RECALL: DEGRADED - no prior incident memory is available.\nContinue without prior-memory evidence.\n",
                    "system": "You return a fixed degraded-evidence marker."
                }
            },
            {
                "id": "language_probe",
                "kind": "agent",
                "with": {
                    "task": "Run a language-specific stack-trace probe. Use the parsed trace and\nevidence gathered so far to propose language-idiomatic checks,\nminimal reproducer shape, and patch targets. Do not claim repository\nevidence that is absent.\n\nLanguage classification:\n{{ outputs.trace_collect | truncate(400) }}\n\nTrace parse:\n{{ outputs.parse_trace | truncate(1200) }}\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(2000) }}\n"
                },
                "route": [
                    {
                        "to": "stack-trace-python-probe",
                        "when": "'\"language\":\"python\"' in outputs.parse_trace or '\"language\": \"python\"' in outputs.parse_trace or 'LANGUAGE: python' in outputs.trace_collect"
                    },
                    {
                        "to": "stack-trace-js-probe",
                        "when": "'\"language\":\"javascript\"' in outputs.parse_trace or '\"language\": \"javascript\"' in outputs.parse_trace or '\"language\":\"typescript\"' in outputs.parse_trace or '\"language\": \"typescript\"' in outputs.parse_trace or 'LANGUAGE: javascript' in outputs.trace_collect or 'LANGUAGE: typescript' in outputs.trace_collect"
                    },
                    {
                        "to": "stack-trace-go-probe",
                        "when": "'\"language\":\"go\"' in outputs.parse_trace or '\"language\": \"go\"' in outputs.parse_trace or 'LANGUAGE: go' in outputs.trace_collect"
                    },
                    {
                        "to": "stack-trace-rust-probe",
                        "when": "'\"language\":\"rust\"' in outputs.parse_trace or '\"language\": \"rust\"' in outputs.parse_trace or 'LANGUAGE: rust' in outputs.trace_collect"
                    }
                ],
                "skill": "stack-trace-generic-probe",
                "depends_on": [
                    "parse_trace"
                ]
            },
            {
                "id": "root_cause",
                "kind": "llm_chat",
                "with": {
                    "task": "Synthesize a root-cause hypothesis from these parallel\ninvestigations and the original trace parse.\n\nTrace parse:\n{{ outputs.parse_trace | truncate(600) }}\n\nRepo grep:\n{{ outputs.grep_repo | truncate(1200) }}\n\nRelated GH issues:\n{{ outputs.search_issues | truncate(800) }}\n\nRecent commits on affected files:\n{{ outputs.git_history | truncate(800) }}\n\nCurrent git diff context:\n{{ outputs.diff_context | truncate(1200) }}\n\nPrior OpenSquilla skill\/router history patterns:\n{{ outputs.history_patterns | truncate(1200) }}\n\nPrior similar incidents (may be empty on a fresh install — if\nthis section is empty or returns no matches, IGNORE it and\nsynthesize the root cause from the other available investigations\nalone; do not invent prior incidents that are not listed):\n{{ outputs.memory_recall | truncate(800) }}\n\nTreat prior memory as a non-authoritative search hint only. Do not\ncite memory paths, similarity scores, or prior-incident claims as\nevidence for the current traceback; the current trace and target\nrepository evidence are the only grounding sources.\n\nLanguage-specific probe:\n{{ outputs.language_probe | truncate(1200) }}\n\nIf repository search returned NO_HITS or the referenced files are\nabsent, still derive a bounded hypothesis from the stack trace\ncontract itself. Clearly say the repository evidence is degraded;\ndo not pretend that files or symbols were inspected.\n\nException-semantics guard:\n- For Python expressions like json.loads(raw)['result'] with\n  KeyError: 'result', treat the decoded value as a mapping\/dict\n  missing that top-level key. Do not rank list\/string\/null\/non-JSON\n  payloads as primary causes; put them under rejected\/different\n  exception shapes because they would normally raise TypeError or\n  JSONDecodeError instead.\n- When repository evidence is degraded, the strongest evidence is\n  the consumer contract violation: parser expects result, producer\n  supplied another valid JSON object shape.\n\nReply with this exact structure (no preamble):\n\nEXCEPTION_SEMANTICS: <what the exception class implies for this exact expression; name payload shapes that would and would not cause it>\nROOT_CAUSE: <one-sentence highest-likelihood hypothesis>\nEVIDENCE:\n  - <which investigation supported it; cite line>\n  - <which investigation supported it; cite line>\nRANKED_HYPOTHESES:\n  - likelihood=<high|medium|low>; cause=<cause>; evidence=<evidence>; falsify=<command\/check>\n  - include at least six bounded hypotheses when repository\n    evidence is degraded; cover error envelopes, schema drift,\n    nested result wrappers, streaming\/control frames, wrong\n    dispatcher\/message type, and provider\/transport rewraps when\n    they fit the exception semantics\nSUGGESTIONS:\n  - <file:line> — <action>\n  - <file:line> — <action>\n  - <file:line> — <action>\n",
                    "system": "You synthesize bounded root-cause hypotheses from stack traces, exception semantics, and explicit evidence."
                },
                "depends_on": [
                    "grep_repo",
                    "search_issues",
                    "git_history",
                    "diff_context",
                    "history_patterns",
                    "memory_recall",
                    "language_probe"
                ]
            },
            {
                "id": "repro_suggestion",
                "kind": "llm_chat",
                "with": {
                    "task": "Propose the smallest safe verification command(s) for this root-cause\nhypothesis. Prefer existing tests, targeted unit tests, or a minimal\nreproducer command. Do not propose destructive commands. Do not\npropose commands that create, overwrite, or edit files, including\nheredocs, shell redirection, `cat >`, `tee`, `python - <<`,\n`python -c` that writes files, or temporary files under `\/tmp`.\nIf a reproducer needs code, include it as an inline snippet marked\n\"copy into an existing test file\" and keep Verification commands\nlimited to read-only locate\/history checks or existing test commands.\n\nLanguage classification:\n{{ outputs.trace_collect | truncate(400) }}\n\nTrace parse:\n{{ outputs.parse_trace | truncate(600) }}\n\nRoot-cause report:\n{{ outputs.root_cause | truncate(1200) }}\n\nLanguage-specific probe:\n{{ outputs.language_probe | truncate(1200) }}\n\nReply with:\nCONFIDENCE: <low|medium|high>\nVERIFY:\n  - <command or manual check>\n  - <minimal reproducer command or snippet for the parsed language>\n  - <history\/blame or producer-consumer schema check>\nFIX_FIRST:\n  - <first file\/action>\nPATCH_SHAPE:\n  - <specific defensive-code shape to try first>\n  - <schema-normalization or frame-filtering shape if relevant>\n\nFor parser\/envelope failures, prefer a protocol-error branch plus\nfixture-driven contract tests over silently returning a default or\nfabricated result. If a fallback\/retry is useful, phrase it as a\ncaller policy after logging and typed error classification, not as\nthe parser's default behavior.\n",
                    "system": "You propose safe, minimal verification commands for debugging."
                },
                "depends_on": [
                    "root_cause"
                ]
            },
            {
                "id": "degraded_summary",
                "kind": "llm_chat",
                "with": {
                    "task": "CRITICAL OUTPUT CONTRACT:\n- First line must be exactly: ## Trace Facts\n- Use the same language as the original user request. If the\n  original request is in English, answer in English.\n- Do not include an opening acknowledgement, apology, emoji, or\n  process commentary.\n- Do not include the words \"meta-skill\", \"search step\", \"path\n  restriction\", \"internal tool\", \"memory persistence\", \"saved\",\n  \"git_history\", \"DAG\", \"memory\/traceback.md\", \"prior incident\",\n  \"similarity score\", or \"step\".\n- If repository evidence is unavailable, write only:\n  \"Repository evidence: DEGRADED in this benchmark\/workspace; run\n  the commands below in the target repository.\"\n- Verification Commands must contain only commands\/checks. Code\n  changes belong only in Patch Direction, not Verification Commands.\n  Never include file-creation or file-edit commands in Verification\n  Commands: no heredocs, no shell redirection, no `cat >`, no `tee`,\n  no `python - <<`, no `python -c` file writes, and no `\/tmp`\n  scratch-file creation. Reproducer code belongs in the Reproduction\n  section as an inline snippet, not as a command that writes files.\n- Keep the final report compact enough to finish: cap root-cause\n  matrix rows at 8, reproducer rows at 5, and patch-direction bullets\n  at 6. Prefer dense commands and bullets over long prose.\n- Patch Direction must complete before Related Checks. Do not spend\n  the token budget on full implementation code unless the user asked\n  for a patch.\n- For parser\/envelope failures, do not recommend returning a default\n  success\/error object from the parser as the first fix. Prefer typed\n  protocol\/execution errors, payload-key logging, schema\n  normalization only for supported legacy success keys, and\n  fixture-driven contract tests.\n- Verification Commands must include at least one exact import-path\n  reproducer when a parsed file\/module path is available, plus at\n  least one targeted pytest command for the parser\/envelope contract.\n- Do not ask follow-up questions at the end.\n\nProduce the final user-facing investigation. If any evidence source\nreturned NO_HITS, NO_MATCHING_ISSUES, NO_RECENT_COMMITS, auth errors,\nor empty memory, label that source as DEGRADED instead of hiding it.\nThis is the final answer shown to the user: do not mention\nmeta-skill step ids, memory persistence, internal tools, or that\nanything was saved. Do not say \"the meta-skill search step hit a path\nrestriction\"; phrase unavailable repo evidence only inside Evidence\nStatus.\nTreat raw errors from repository\/history tools as private diagnostic\nnoise. Do not quote them, translate them, or identify which internal\nlookup failed; collapse them to the generic degraded evidence line\nabove.\nTreat memory recall as a private hint source. Never include a\n\"Prior incident\" evidence row, memory path, memory score, or memory\ncitation in the final report.\n\nWhen repository evidence is degraded, do not stop at a short\nconclusion. Provide a useful fallback investigation based on the\ntrace contract:\n- say that the referenced files\/symbols were not found in the\n  current workspace when that is true;\n- include exact repo search commands the user can run in the real\n  target repository;\n- include a minimal reproducer snippet or command for the parsed\n  language\/runtime;\n- include a defensive patch direction with expected failure mode;\n- include exact verification commands.\n\nQuality bar for user-facing output:\n- start with trace facts, not process commentary\n- parse the failing frame precisely: file, line, function, expression,\n  exception class, and what the exception proves\n- explicitly state the data-shape implications:\n  json.loads(raw) succeeded; the decoded payload was subscriptable\n  by string key; the top-level key \"result\" was absent\n- explicitly reject payload shapes that would produce JSONDecodeError,\n  TypeError, or IndexError instead of the observed exception\n- for Python, do not say list\/string\/null payloads would cause\n  KeyError for this expression; they are rejected\/different-exception\n  shapes unless extra wrapping evidence exists\n- rank a broad hypothesis matrix, including schema drift, error\n  envelope, nested result, streaming\/control frame, wrong dispatcher,\n  transport\/provider rewrap, and renamed key when applicable\n- include at least seven ranked hypotheses when repository evidence\n  is degraded: error envelope, schema\/version drift, streaming or\n  partial frame, wrong dispatcher\/message type, renamed\/cased key,\n  exception serialized as tool output, and empty\/null\/stripped result\n- include a hypothesis-driven reproducer matrix for at least four\n  payload shapes: success envelope, error envelope, streaming\/control\n  frame, and non-dict JSON; specify expected exception or output\n- repo search targets must include producer, consumer, schema\/types,\n  transport wrappers, streaming\/chunking, fixtures\/logs, tests, git\n  history, and blame\n- verification commands must be exact shell commands and must include\n  rg checks, git log\/blame checks, a minimal language-specific\n  reproducer, and targeted test commands\n- the minimal language-specific reproducer must be an inline snippet\n  plus an existing-test command, not a file-creation command; do not\n  use `cat >`, heredocs, redirection, `tee`, `\/tmp` files, or\n  `python - <<` in Verification Commands\n- prioritize producer-adapter checks and contract tests over broad\n  generic advice; tie each verification command to the failing module\n  path, symbol, or envelope contract when possible\n- Patch Direction should distinguish:\n  1. parser boundary: decode, type check, error-envelope branch,\n     supported success-key normalization, typed protocol error\n  2. producer adapters: guarantee one success envelope shape\n  3. caller\/runtime: catch typed failures and log tool identity\n  4. tests: fixtures for success, error envelope, missing key,\n     streaming\/control frame, and non-dict JSON\n- include these concrete search families when applicable:\n  `rg -nF \"parse_tool_result\"`, `rg -n \"tool_call|tool_result|dispatch|invoke_tool\"`,\n  `rg -n \"stream|chunk|delta|partial\"`, `rg -n \"openai|anthropic|mcp|jsonrpc\"`,\n  `rg -nP \"return\\s*\\{\\s*['\\\"](result|data|output|content|error|status|message)['\\\"]\" src\/`,\n  `rg -nP \"json\\.loads\\([^)]+\\)\\[['\\\"][^'\\\"]+['\\\"]\\]\" src\/`,\n  `git log -p --since=\"60 days\" -- <files>`, and `git blame -L`\n- state that commands are recommended next steps, not executed\n- do not end by asking whether the user wants more detail\n\nRoot cause:\n{{ outputs.root_cause | truncate(1200) }}\n\nTrace parse:\n{{ outputs.parse_trace | truncate(800) }}\n\nLanguage classification:\n{{ outputs.trace_collect | truncate(400) }}\n\nVerification plan:\n{{ outputs.repro_suggestion | truncate(1000) }}\n\nLanguage-specific probe:\n{{ outputs.language_probe | truncate(1000) }}\n\nEvidence availability:\n- Repository\/history\/issue evidence may be unavailable in benchmark\n  workspaces. If the prior sections do not contain concrete\n  file-line excerpts from the target repository, use the exact\n  degraded evidence sentence from the contract and continue with a\n  trace-contract investigation.\n- Do not quote raw lookup errors, internal lookup names, or protected\n  path details.\n\nReply in Markdown with exactly these sections and no preamble:\n## Trace Facts\n## Diagnosis\n## Exception Semantics\nExplain what the exception class means for the exact failing\nexpression. Reject payload shapes that would produce a different\nexception type.\n## Evidence Status\n## Assumptions \/ Constraints\n## Ranked Root Cause Matrix\nInclude at least seven rows. Each row must include likelihood,\nevidence, falsifying command\/check, and expected signal.\n## Repo Search Targets\nGroup searches by direct hits, producer\/wrappers, runtime\/streaming,\nschema\/types, tests\/fixtures\/logs, and git history. Prefer `rg` and\ninclude exact commands. Do not assume `src\/tools\/` exists; use\nrepo-wide commands first, then path-specific commands for parsed\nframes.\n## Reproduction\nInclude a minimal fixture or snippet for the parsed language\/runtime\nplus a small matrix of payload shapes and expected outcomes.\n## Patch Direction\nKeep this section concise and complete. Prefer bullet-level patch\ntargets and contract-test shape over long code blocks. Explicitly\nreject silent default-return behavior for missing required result\nkeys unless the target repository's protocol already documents that\nbehavior.\n## Patch Target Checklist\n## Related Checks\nInclude adjacent contract checks: raw payload logging, error-path\ntests, schema validation, all return sites funneled through one\nwrapper, retry\/fallback behavior, stream assembly, raw type\nnarrowing, sibling unsafe `json.loads(...)[key]` patterns, and\nmissing observability around tool identity \/ tool_call_id \/\nproducer name \/ payload keys at the parser boundary.\n## Verification Commands\nSeparate locate\/context commands, producer-shape search, unit tests,\nisolated reproducer commands, static sweeps, logs, and history\nchecks. Include commands for the happy path, error-envelope path,\nstreaming\/control-frame path, non-dict path, and tool-identity\nlogging check. Use only read-only searches\/history\/log commands and\nexisting test commands. Do not include commands that create or edit\nfiles; if a new fixture is needed, describe the fixture in Patch\nDirection or Reproduction instead.",
                    "system": "You are a strict final-report renderer for stack-trace investigations. Return only the final report. Never mention internal orchestration, tool failures, path restrictions, memory persistence, or saved artifacts."
                },
                "depends_on": [
                    "grep_repo",
                    "search_issues",
                    "git_history",
                    "diff_context",
                    "history_patterns",
                    "memory_recall",
                    "language_probe",
                    "repro_suggestion"
                ]
            },
            {
                "id": "persist",
                "kind": "tool_call",
                "tool": "memory_save",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/traceback.md",
                    "content": "=== stack-trace investigation ===\nparse: {{ outputs.parse_trace | truncate(400) }}\nhypothesis: {{ outputs.degraded_summary | truncate(1000) }}"
                },
                "depends_on": [
                    "degraded_summary"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when the user gives a stack trace, traceback, runtime error, or failing log that benefits from multi-skill orchestration across trace parsing, repo\/history inspection, patch-target analysis, reproduction guidance, and verification commands.",
    "meta_priority": 60,
    "final_text_mode": "step:degraded_summary"
}

{
    "kind": "meta",
    "name": "meta-travel-planner",
    "always": false,
    "triggers": [
        "travel plan",
        "trip plan",
        "trip itinerary",
        "travel itinerary",
        "day-by-day travel",
        "days in",
        "day in",
        "plan my trip",
        "plan our trip",
        "plan a trip",
        "itinerary for",
        "旅游计划",
        "出差行程",
        "行程安排",
        "规划行程",
        "帮我安排",
        "怎么玩",
        "做个行程"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "trip_collect",
                "kind": "llm_chat",
                "with": {
                    "task": "Extract a structured trip brief from the original user request.\nDo NOT ask the user to confirm details that are already stated or\nsafely inferable. If a value is missing, make a conservative\nassumption and mark it as ASSUMED, except only when destination or trip length is absent.\nIn that case set NEEDS_CLARIFICATION: yes so the next step can pause\nand ask for the missing critical details.\n\nDo not invent exact calendar dates, weekdays, booking status, or\nweather probabilities from vague timing such as \"late June\" or\n\"sometime next year\". Preserve the user's wording when the year or\nexact dates are absent.\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(1400) }}\n\nReturn exactly:\nDESTINATION: <city\/region, or ASSUMED: ...>\nDAYS: <integer or ASSUMED: ...>\nDATES: <date range\/season, or ASSUMED: ...>\nPARTY: <party size\/type, or ASSUMED: ...>\nBUDGET: <budget|mid|premium, or ASSUMED: mid>\nPACE: <relaxed|balanced|packed>\nINTERESTS:\n  - <interest>\nMUST_INCLUDE:\n  - <explicit user requirement>\nNEEDS_CLARIFICATION: <yes|no>\nMISSING_FIELDS:\n  - <destination|days|none>\nCLARIFY_REASON: <one concise reason, or none>\nASSUMPTIONS:\n  - <assumption>\n",
                    "system": "You extract travel requirements without asking a follow-up unless the destination or trip length is genuinely absent."
                }
            },
            {
                "id": "trip_clarify",
                "kind": "user_input",
                "when": "'NEEDS_CLARIFICATION: yes' in outputs.trip_collect",
                "clarify": {
                    "mode": "form",
                    "intro": "行程关键条件还不完整。请补齐目的地和天数；如果已有信息不变，可以重复填写。\n",
                    "fields": [
                        {
                            "name": "destination",
                            "type": "string",
                            "prompt": "目的地 \/ Destination",
                            "required": true,
                            "max_chars": 120
                        },
                        {
                            "max": 60,
                            "min": 1,
                            "name": "days",
                            "type": "int",
                            "prompt": "旅行天数 \/ Number of days",
                            "required": true
                        },
                        {
                            "name": "dates",
                            "type": "string",
                            "prompt": "日期或季节 \/ Dates or season",
                            "max_chars": 120
                        },
                        {
                            "name": "party",
                            "type": "string",
                            "prompt": "同行人和人数 \/ Party size",
                            "max_chars": 120
                        },
                        {
                            "name": "must_include",
                            "type": "string",
                            "prompt": "必须包含或避开的事项 \/ Must include or avoid",
                            "max_chars": 300
                        }
                    ],
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "算了",
                        "取消",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "depends_on": [
                    "trip_collect"
                ]
            },
            {
                "id": "trip_preferences",
                "kind": "llm_chat",
                "with": {
                    "task": "Expand the extracted travel facts into a full planning contract.\nNever return a clarification question. If facts are uncertain, keep\nthe assumption explicit and continue with a practical default.\nPrefer explicit clarification answers over first-pass assumptions.\n\nExtracted facts:\n{{ outputs.trip_collect | truncate(1200) }}\n\nClarification answers (may be empty when not needed):\n{{ inputs.get('collected', {}).get('trip_clarify', {}) | tojson }}\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nReturn exactly:\nDESTINATION: <city\/region>\nDATES: <duration, date range, season, or ASSUMED value>\nPARTY: <party size\/type>\nBUDGET: <budget level>\nPACE: <relaxed|balanced|packed>\nINTERESTS:\n  - <interest>\nCONSTRAINTS:\n  - <constraint or assumption>\n",
                    "system": "You expand extracted travel facts into a structured planning contract."
                },
                "depends_on": [
                    "trip_collect",
                    "trip_clarify"
                ]
            },
            {
                "id": "weather",
                "kind": "skill_exec",
                "with": {
                    "days": 3,
                    "location": "{{ outputs.trip_preferences | truncate(512) }}",
                    "max_chars": 2200
                },
                "skill": "weather",
                "depends_on": [
                    "trip_preferences"
                ]
            },
            {
                "id": "poi",
                "kind": "skill_exec",
                "with": {
                    "query": "{{ outputs.trip_preferences | truncate(512) }} sights restaurants transport hours neighborhoods",
                    "engines": [
                        "brave",
                        "duckduckgo"
                    ],
                    "max_results": 15
                },
                "skill": "multi-search-engine",
                "depends_on": [
                    "trip_preferences"
                ]
            },
            {
                "id": "constraints",
                "kind": "llm_chat",
                "with": {
                    "task": "Extract itinerary constraints from weather and POI results: opening\nhours, transit time assumptions, weather risks, neighborhoods to\ngroup together, and any likely booking constraints.\n\nEvidence boundary:\n- Weather tools often return short-range\/current forecasts. If the\n  trip timing is vague, seasonal, or outside the forecast window, do\n  not convert current weather into trip-day probabilities. Use\n  seasonal risk language and mark exact forecast as unavailable.\n- If POI search is thin or missing, do not list restaurants, opening\n  hours, events, or booking requirements as verified.\n- Preserve explicit mobility, dietary, fixed-booking, budget, and\n  rest constraints before adding optional attractions.\n\nPreferences:\n{{ outputs.trip_preferences | truncate(1200) }}\n\nWeather:\n{{ outputs.weather | truncate(2000) }}\n\nPOI search:\n{{ outputs.poi | truncate(6000) }}\n",
                    "system": "You convert weather and search results into itinerary constraints."
                },
                "depends_on": [
                    "weather",
                    "poi"
                ]
            },
            {
                "id": "itinerary",
                "kind": "llm_chat",
                "with": {
                    "task": "Build the primary day-by-day itinerary. It must be complete enough\nto use without reading any later step.\n\nInclude:\n- assumptions\n- one section per day with morning \/ afternoon \/ evening\n- neighborhood grouping and transit notes\n- food suggestions\n- rain-aware risks and substitutions\n- rough budget notes\n\nTrip preferences:\n{{ outputs.trip_preferences | truncate(1200) }}\n\nWeather forecast:\n{{ outputs.weather | truncate(2000) }}\n\nPOI search:\n{{ outputs.poi | truncate(5000) }}\n\nConstraints:\n{{ outputs.constraints | truncate(3000) }}\n",
                    "system": "You write complete, practical travel itineraries. Return only the itinerary."
                },
                "depends_on": [
                    "constraints"
                ]
            },
            {
                "id": "final_plan",
                "kind": "llm_chat",
                "with": {
                    "task": "Assemble the complete travel product. Do not return only variants.\nDo not include process commentary.\nReturn every required section. Keep the whole answer compact enough\nto fit in one model response: 4,500-6,500 characters is preferred.\nIf space is tight, shorten day descriptions before omitting the\nvariants, evidence, next-step, or artifact sections.\n\nRequired sections:\n1. Assumptions\n2. Primary itinerary matching the requested or inferred trip length\n3. Weather-aware risks and rain backups\n4. Variants\n5. Budget and booking notes\n6. Evidence and source notes\n7. Next steps\n\nPreserve concrete timings, neighborhoods, transit grouping, food\nideas, weather constraints, and budget constraints. Do not open with\n\"I researched\" or imply live verification unless a tool result is\nshown in the evidence notes. Do not invent exact trip calendar dates,\nweekdays, or daily rain percentages from vague timing such as\n\"late June\" unless the user supplied exact dates and weather evidence\ncovers those dates. If weather evidence is short-range\/current but\nthe trip is future or seasonal, say \"seasonal planning assumption\"\nrather than \"forecast\". Keep each day to 5-7 highly actionable\nbullets or a compact schedule. Include:\n- a Route spine line for each day, e.g. Neighborhood A -> B -> C\n- no more than 2-3 main anchors per day unless the user requested a\n  packed pace\n- an explicit pacing note: relaxed\/balanced\/packed and what to skip\n  if tired\n- one rest block or pacing reset per day for balanced\/relaxed trips\n- transit-coherent neighborhood adjacency; avoid cross-city zigzags\n- relaxed version\n- efficient\/packed version\n- bad-weather backup\n- weather switch points: if rain\/heavy heat, swap X for Y, framed as\n  seasonal risk unless exact forecast evidence covers the trip dates\n- rough daily budget notes as ranges and flex levers, not false\n  precision\n- specific checks before booking, including opening-hours checks,\n  timed-entry reservations, and transit-pass choice\n- mark specific restaurants, opening hours, and seasonal events as\n  \"verify before booking\" unless they came from explicit search\n  evidence in this run\n- omit artifact generation suggestions unless the user explicitly\n  asked for an artifact or file\n\nIf search or weather evidence is thin, state assumptions plainly\ninstead of inventing sources. Include map\/search links only as\nplain URLs when useful. Use the words Evidence, Source notes,\nReference checks, Next steps, Verify, HTML, and Report\nonly where they fit naturally in the final sections.\n\nItinerary:\n{{ outputs.itinerary | truncate(7000) }}\n\nConstraint notes:\n{{ outputs.constraints | truncate(2500) }}\n\nWeather evidence:\n{{ outputs.weather | truncate(1600) }}\n\nPOI\/source notes:\n{{ outputs.poi | truncate(2000) }}",
                    "system": "You assemble complete travel plans for users. Return only the final answer."
                },
                "depends_on": [
                    "itinerary",
                    "constraints",
                    "weather",
                    "poi"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when the user needs a trip plan, travel itinerary, business-trip schedule, or day-by-day travel brief that benefits from multi-skill orchestration across preference inference, weather, place search, constraint extraction, itinerary drafting, variants, and optional artifact guidance.",
    "meta_priority": 50,
    "final_text_mode": "step:final_plan"
}

{
    "kind": "meta",
    "name": "meta-web-to-pdf-briefing",
    "always": false,
    "triggers": [
        "pdf briefing",
        "PDF 简报"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "search",
                "with": {
                    "query": "{{ inputs.user_message | xml_escape | truncate(512) }}",
                    "engines": [
                        "brave",
                        "tavily",
                        "duckduckgo"
                    ],
                    "max_results": 10
                },
                "skill": "multi-search-engine"
            },
            {
                "id": "digest",
                "with": {
                    "text": "{{ outputs.search }}",
                    "style": "bulleted",
                    "max_words": 600
                },
                "skill": "summarize",
                "depends_on": [
                    "search"
                ]
            },
            {
                "id": "render",
                "with": {
                    "html": "<!DOCTYPE html>\n<html><head><meta charset=\"utf-8\"><title>{{ inputs.user_message | xml_escape | truncate(128) }}<\/title><\/head>\n<body>\n  <h1>{{ inputs.user_message | xml_escape | truncate(128) }}<\/h1>\n  <article>{{ outputs.digest | xml_escape }}<\/article>\n<\/body><\/html>\n",
                    "page_size": "A4"
                },
                "skill": "html-to-pdf",
                "depends_on": [
                    "digest"
                ]
            }
        ]
    },
    "description": "Render a topic into a distributable PDF briefing in three steps: web search → bullet summary → styled PDF. Trigger when the user asks for a PDF briefing on a single topic.",
    "meta_priority": 50
}

{
    "kind": "meta",
    "name": "meta-paper-write",
    "always": false,
    "triggers": [
        "写论文",
        "draft a paper",
        "写篇论文",
        "write paper"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "paper_preferences",
                "kind": "agent",
                "with": {
                    "user_message": "{{ inputs.user_message | xml_escape | truncate(1200) }}"
                },
                "skill": "paper-preference-planner"
            },
            {
                "id": "search_papers",
                "kind": "skill_exec",
                "skill": "multi-search-engine",
                "depends_on": [
                    "paper_preferences"
                ]
            },
            {
                "id": "experiment",
                "kind": "skill_exec",
                "skill": "paper-experiment-stub",
                "depends_on": [
                    "paper_preferences"
                ]
            },
            {
                "id": "refbib",
                "kind": "skill_exec",
                "skill": "paper-refbib-stub",
                "depends_on": [
                    "search_papers"
                ]
            },
            {
                "id": "source_pack",
                "kind": "agent",
                "with": {
                    "topic": "{{ inputs.user_message | xml_escape | truncate(200) }}",
                    "bibliography": "{{ outputs.refbib | truncate(8000) }}",
                    "search_results": "{{ outputs.search_papers | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-source-curator",
                "depends_on": [
                    "search_papers",
                    "refbib"
                ]
            },
            {
                "id": "outline",
                "kind": "agent",
                "with": {
                    "topic": "{{ inputs.user_message | xml_escape | truncate(200) }}",
                    "source_pack": "{{ outputs.source_pack | truncate(8000) }}",
                    "cite_keys_hint": "{{ outputs.refbib | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-outline-author",
                "depends_on": [
                    "source_pack"
                ]
            },
            {
                "id": "citation_plan",
                "kind": "agent",
                "with": {
                    "topic": "{{ inputs.user_message | xml_escape | truncate(200) }}",
                    "outline": "{{ outputs.outline }}",
                    "source_pack": "{{ outputs.source_pack | truncate(8000) }}",
                    "bibliography": "{{ outputs.refbib | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-citation-planner",
                "depends_on": [
                    "outline",
                    "source_pack",
                    "refbib"
                ]
            },
            {
                "id": "plot",
                "kind": "skill_exec",
                "skill": "paper-plot-stub",
                "depends_on": [
                    "experiment"
                ]
            },
            {
                "id": "draft_intro",
                "kind": "agent",
                "with": {
                    "outline": "{{ outputs.outline }}",
                    "section": "introduction",
                    "citation_plan": "{{ outputs.citation_plan | truncate(8000) }}",
                    "cite_keys_hint": "{{ outputs.refbib | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-section-author",
                "depends_on": [
                    "outline",
                    "citation_plan",
                    "refbib",
                    "plot"
                ]
            },
            {
                "id": "draft_method",
                "kind": "agent",
                "with": {
                    "outline": "{{ outputs.outline }}",
                    "section": "method",
                    "citation_plan": "{{ outputs.citation_plan | truncate(8000) }}",
                    "cite_keys_hint": "{{ outputs.refbib | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-section-author",
                "depends_on": [
                    "outline",
                    "citation_plan",
                    "refbib",
                    "plot"
                ]
            },
            {
                "id": "draft_results",
                "kind": "agent",
                "with": {
                    "outline": "{{ outputs.outline }}",
                    "section": "results",
                    "figure_path": "paper\/figure_1.pdf",
                    "citation_plan": "{{ outputs.citation_plan | truncate(8000) }}",
                    "cite_keys_hint": "{{ outputs.refbib | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-section-author",
                "depends_on": [
                    "outline",
                    "citation_plan",
                    "refbib",
                    "plot"
                ]
            },
            {
                "id": "draft_discussion",
                "kind": "agent",
                "with": {
                    "outline": "{{ outputs.outline }}",
                    "section": "discussion",
                    "citation_plan": "{{ outputs.citation_plan | truncate(8000) }}",
                    "cite_keys_hint": "{{ outputs.refbib | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-section-author",
                "depends_on": [
                    "outline",
                    "citation_plan",
                    "refbib",
                    "plot"
                ]
            },
            {
                "id": "revised_body",
                "kind": "agent",
                "with": {
                    "topic": "{{ inputs.user_message | xml_escape | truncate(200) }}",
                    "method": "{{ outputs.draft_method }}",
                    "outline": "{{ outputs.outline }}",
                    "results": "{{ outputs.draft_results }}",
                    "discussion": "{{ outputs.draft_discussion }}",
                    "introduction": "{{ outputs.draft_intro }}",
                    "citation_plan": "{{ outputs.citation_plan | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-revision-author",
                "depends_on": [
                    "draft_intro",
                    "draft_method",
                    "draft_results",
                    "draft_discussion"
                ]
            },
            {
                "id": "draft_abstract",
                "kind": "agent",
                "with": {
                    "topic": "{{ inputs.user_message | xml_escape | truncate(200) }}",
                    "revised_body": "{{ outputs.revised_body | truncate(8000) }}",
                    "citation_plan": "{{ outputs.citation_plan | truncate(8000) }}",
                    "paper_preferences": "{{ outputs.paper_preferences | truncate(4000) }}"
                },
                "skill": "paper-abstract-author",
                "depends_on": [
                    "revised_body",
                    "citation_plan"
                ]
            },
            {
                "id": "compile_latex",
                "kind": "skill_exec",
                "skill": "latex-compile",
                "depends_on": [
                    "draft_abstract"
                ]
            }
        ]
    },
    "description": "Draft a demo research paper end-to-end from a topic phrase: preference planning → web search → source curation → BibTeX → citation plan → topic-aware outline → figure → section drafts → global revision → abstract-last → xelatex compile → PDF.",
    "meta_priority": 50
}

{
    "name": "code-task",
    "metadata": {
        "platform": {
            "emoji": "🛠️",
            "install": [],
            "requires": {
                "env": [
                    "OPENROUTER_API_KEY"
                ],
                "bins": [
                    "git"
                ]
            }
        }
    },
    "triggers": [
        "code-task",
        "fix the issue",
        "fix issue",
        "fix the bug",
        "implement this feature",
        "add a function",
        "add a feature",
        "change the code",
        "解决 issue",
        "给项目加功能",
        "给项目加",
        "加个函数",
        "加一个函数",
        "改一下代码",
        "修复仓库",
        "修复 bug"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0",
        "maintained_by": "OpenSquilla"
    },
    "description": "PREFERRED way to change code in a REAL repository: fix a GitHub issue, fix a bug, add\/implement a function or feature, or make any edit to a project the user names by a filesystem path (e.g. \/tmp\/foo, ~\/code\/bar) or a git URL. Clones the repo, runs an OpenSquilla agent on the host to make the change on a task branch, then independently VERIFIES it with a red→green→regression test loop and reports a structured result. STRONGLY prefer this over hand-editing the user's files yourself in this session: editing files directly skips the isolation and the runner-verified red→green proof, so it is not equivalent. Use it whenever the request names a real on-disk repo\/path or a repo URL and asks to fix\/add\/implement\/change code. Examples: 'fix issue 412 in github.com\/acme\/widgets', '给 \/tmp\/calc 加个 average 函数', '帮我改一下 ~\/proj 里的 X', 'implement CSV export in my project'. Docker-free host execution; treat the target repo as TRUSTED. GitHub issue mode needs the `gh` CLI. For self-contained, TESTABLE code from scratch when NO repo is named (e.g. 'write a function that maps A-Z to pitches'), use `--verification-mode scratch` with no --repo: it scaffolds a throwaway project, writes the code plus a test, and verifies it green. Only truly trivial one-liners or conceptual\/non-testable questions are answered inline."
}

{
    "name": "deep-research",
    "homepage": "",
    "metadata": {
        "platform": {
            "emoji": "🔬"
        }
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/in-depth-research",
        "maintained_by": "OpenSquilla"
    },
    "description": "Multi-round research with explicit methodology, evidence tracking, and citation-tagged synthesis. Trigger on 'deep dive', 'research report', 'literature review', 'investigate X across sources', 'multi-round investigation'. Distinct from the `summarize` skill, which is a single-pass condensation; this skill maintains a state file across iterations, tracks coverage, and produces a long-form report with per-claim citations. Three execution stages: plan (scope into sub-questions), iterate (record evidence per round), compile (synthesize report). The skill itself does not fetch the web — it tells the host agent which fetches to perform via OpenSquilla's existing web tools, and records what comes back."
}

{
    "kind": "meta",
    "name": "meta-kid-project-planner",
    "always": false,
    "metadata": {
        "opensquilla": {
            "risk": "medium",
            "capabilities": [
                "network",
                "filesystem-write"
            ],
            "clawhub_top100_composition": [
                {
                    "rank": 11,
                    "role": "Find safe, age-appropriate how-to references and material alternatives.",
                    "skill": "Multi Search Engine",
                    "local_skill": "multi-search-engine",
                    "rank_source": "Top ClawHub Skills downloads top100, 2026-05-28"
                },
                {
                    "role": "Plan outdoor child projects around realistic weather constraints.",
                    "skill": "Weather",
                    "local_skill": "weather",
                    "rank_source": "Top ClawHub Skills downloads top100, 2026-05-28"
                },
                {
                    "role": "Add extra safety and feasibility research when the project is more complex.",
                    "skill": "Deep Researcher \/ deep research family",
                    "local_skill": "deep-research",
                    "rank_source": "ClawHub research-skill family, verified via current search results"
                },
                {
                    "role": "Produce kid-facing printable step cards or a simple presentation when requested.",
                    "skill": "PowerPoint \/ PPTX",
                    "local_skill": "pptx",
                    "rank_source": "Top ClawHub Skills downloads top100, 2026-05-28"
                }
            ]
        }
    },
    "triggers": [
        "school project",
        "science fair",
        "kid science",
        "孩子做项目",
        "做一个手工",
        "科学课作业",
        "help my kid build",
        "我要做火山",
        "child diy project",
        "课外动手项目"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "preferences",
                "kind": "llm_chat",
                "with": {
                    "task": "Extract the kid-project brief.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1600) }}\n\nClarification policy:\n- If the request already includes a project topic, child age or age\n  band, and deadline or rough time window, set\n  NEEDS_CLARIFICATION: no and MISSING_FIELDS: none.\n- Budget, exact presentation format, exact weather, and exact\n  sunshine direction can remain explicitly unknown assumptions; do\n  not block on them and do not invent precise values.\n- For common school projects, proceed with explicit assumptions or\n  UNKNOWN markers instead of asking a form unless safety or the core\n  topic is unclear.\n\nReturn exactly:\nTOPIC: <short project description>\nAGE_BAND: <PRE_K|EARLY_GRADE|TWEEN|TEEN|UNKNOWN>\nDEADLINE_DAYS: <integer days until due, or UNKNOWN>\nBUDGET_BAND: <SHOESTRING|MODEST|COMFORTABLE|UNKNOWN>\nPARENT_SUPERVISION: <SOLO|LIGHT|HANDS_ON|UNKNOWN>\nLANGUAGE: <en|zh|mixed>\nPROJECT_SAFE: <yes|no>\nUNSAFE_REASON: <one phrase if PROJECT_SAFE is no, else none>\nNEEDS_CLARIFICATION: <yes|no>\nMISSING_FIELDS:\n  - <topic|age_band|deadline|none>\nASSUMPTIONS:\n  - <assumption>\n",
                    "system": "You extract kid-project preferences. Return only the requested contract. Refuse to plan projects that are clearly unsafe (firearms, fireworks, drugs, sharp-weapon making, etc.) by setting PROJECT_SAFE: no."
                },
                "label": "偏好提取",
                "label_en": "Preference extraction"
            },
            {
                "id": "project_clarify",
                "kind": "user_input",
                "when": "'NEEDS_CLARIFICATION: yes' in outputs.preferences and 'PROJECT_SAFE: yes' in outputs.preferences and 'MISSING_FIELDS:\n  - none' not in outputs.preferences",
                "label": "项目澄清",
                "clarify": {
                    "mode": "form",
                    "intro": "再确认几件事，然后给你完整的项目规划（含家长版） \/ A few details and I'll build the kid + parent pack.\n",
                    "fields": [
                        {
                            "name": "topic",
                            "type": "string",
                            "prompt": "项目主题（如：做一座火山模型）\/ Project topic",
                            "required": true,
                            "max_chars": 200,
                            "prompt_en": "Project topic",
                            "prompt_zh": "项目主题（例如：做一座火山模型）"
                        },
                        {
                            "name": "age_band",
                            "type": "enum",
                            "prompt": "孩子年龄段 \/ Child age band (PRE_K = 3-5, EARLY_GRADE = 6-9, TWEEN = 10-12, TEEN = 13-17)",
                            "choices": [
                                "PRE_K",
                                "EARLY_GRADE",
                                "TWEEN",
                                "TEEN"
                            ],
                            "required": true,
                            "prompt_en": "Child age band (PRE_K = 3-5, EARLY_GRADE = 6-9, TWEEN = 10-12, TEEN = 13-17)",
                            "prompt_zh": "孩子年龄段（PRE_K=3-5 岁，EARLY_GRADE=6-9 岁，TWEEN=10-12 岁，TEEN=13-17 岁）"
                        },
                        {
                            "max": 365,
                            "min": 0,
                            "name": "deadline_days",
                            "type": "int",
                            "prompt": "几天后要交（0 = 今天，14 = 两周）\/ Days until due",
                            "default": 14,
                            "prompt_en": "Days until due",
                            "prompt_zh": "几天后要交（0 = 今天，14 = 两周）"
                        },
                        {
                            "name": "budget_band",
                            "type": "enum",
                            "prompt": "预算 \/ Budget",
                            "choices": [
                                "SHOESTRING",
                                "MODEST",
                                "COMFORTABLE"
                            ],
                            "default": "MODEST",
                            "prompt_en": "Budget",
                            "prompt_zh": "预算"
                        },
                        {
                            "name": "parent_supervision",
                            "type": "enum",
                            "prompt": "家长参与程度（SOLO 几乎不参与；LIGHT 偶尔帮一下；HANDS_ON 全程在旁）\/ Parent supervision",
                            "choices": [
                                "SOLO",
                                "LIGHT",
                                "HANDS_ON"
                            ],
                            "default": "LIGHT",
                            "prompt_en": "Parent supervision (SOLO = mostly independent; LIGHT = occasional help; HANDS_ON = close supervision)",
                            "prompt_zh": "家长参与程度（SOLO 几乎不参与；LIGHT 偶尔帮一下；HANDS_ON 全程在旁）"
                        },
                        {
                            "name": "language",
                            "type": "enum",
                            "prompt": "输出语言 \/ Language",
                            "choices": [
                                "en",
                                "zh",
                                "mixed"
                            ],
                            "default": "mixed",
                            "prompt_en": "Output language",
                            "prompt_zh": "输出语言"
                        }
                    ],
                    "intro_en": "A few details and I will build the kid plus guardian project pack.",
                    "intro_zh": "再确认几件事，然后给你完整的项目规划（含家长版）。",
                    "nl_extract": true,
                    "timeout_hours": 48,
                    "cancel_keywords": [
                        "算了",
                        "换个项目",
                        "cancel",
                        "stop"
                    ]
                },
                "label_en": "Project clarification",
                "depends_on": [
                    "preferences"
                ]
            },
            {
                "id": "feasibility",
                "kind": "llm_classify",
                "with": {
                    "text": "Classify project feasibility for this child.\n\nTopic: from preferences \/ clarify.\nPreferences:\n{{ outputs.get('preferences', '') | truncate(800) }}\nClarification:\n{{ inputs.get('collected', {}).get('project_clarify', {}) | tojson }}\n\nDecision rules:\n- INAPPROPRIATE: project involves weapons, fire without\n  supervision possibility, drugs, harmful chemistry,\n  self-harm-adjacent themes, or other clearly unsafe topics\n  for any minor.\n- SAFETY_REVIEW_REQUIRED: project involves heat (small stove,\n  soldering iron), sharp blades (X-Acto knives), electronics\n  with mains voltage, or moderately reactive chemistry. Adult\n  must be present.\n- NEEDS_SHOPPING: project requires materials the household\n  likely does not have (specific kit, model rocket motor,\n  specialty paint).\n- NEEDS_ADULT_HELP: project is age-appropriate but a step\n  requires hands the child does not yet have (cutting balsa\n  wood for a 6-year-old, threading a needle for a 4-year-old).\n- STRAIGHTFORWARD: child can complete the bulk of the project\n  with the declared PARENT_SUPERVISION level.\n"
                },
                "label": "可行性",
                "label_en": "Feasibility",
                "depends_on": [
                    "preferences",
                    "project_clarify"
                ],
                "output_choices": [
                    "STRAIGHTFORWARD",
                    "NEEDS_ADULT_HELP",
                    "NEEDS_SHOPPING",
                    "SAFETY_REVIEW_REQUIRED",
                    "INAPPROPRIATE"
                ]
            },
            {
                "id": "redirect_unsafe",
                "kind": "llm_chat",
                "when": "'PROJECT_SAFE: no' in outputs.preferences or outputs.feasibility == 'INAPPROPRIATE'",
                "with": {
                    "task": "Topic the user asked for:\n{{ inputs.user_message | xml_escape | truncate(400) }}\n\nUnsafe reason (from preferences):\n{{ outputs.get('preferences', '') | truncate(400) }}\n\nWrite:\n1. One sentence acknowledging the curiosity behind the\n   original idea (do not lecture).\n2. One sentence explaining gently why this version isn't a\n   good kid project (concrete, not vague).\n3. Three alternative project ideas that scratch a similar\n   itch but are safe and age-appropriate. Each: one-line\n   topic + one-line \"what the kid will learn \/ make\".\n\nLanguage: match preferences (default zh).\nEnd with a single line:\nUNSAFE_REDIRECT: yes\n",
                    "system": "You write a gentle, non-shaming redirect when a project topic is unsafe or inappropriate. Always offer 3 alternative project ideas that are in the same SPIRIT as the original (curiosity-driven, hands-on, age-appropriate)."
                },
                "label": "安全改写",
                "label_en": "Safety rewrite",
                "depends_on": [
                    "preferences",
                    "feasibility"
                ]
            },
            {
                "id": "recall_past_projects",
                "kind": "tool_call",
                "tool": "memory_search",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "label": "项目召回",
                "label_en": "Project recall",
                "tool_args": {
                    "query": "child project prior projects preferences constraints {{ outputs.get('preferences', '') | truncate(600) }} {{ inputs.user_message | xml_escape | truncate(600) }}",
                    "source": "memory",
                    "max_results": 6
                },
                "depends_on": [
                    "feasibility",
                    "project_clarify"
                ],
                "on_failure": "recall_past_projects_fallback",
                "tool_allowlist": [
                    "memory_search"
                ]
            },
            {
                "id": "recall_past_projects_fallback",
                "kind": "llm_chat",
                "with": {
                    "task": "No durable project memory was read. Continue using only the pasted\nchild age, deadline, materials, budget, supervision, location, and\nproject context. Do not mention runtime errors to the user.\n",
                    "system": "You produce a no-memory fallback note for child project planning."
                },
                "label": "项目召回兜底",
                "label_en": "Project recall fallback"
            },
            {
                "id": "web_research",
                "kind": "skill_exec",
                "when": "outputs.feasibility in ['STRAIGHTFORWARD', 'NEEDS_ADULT_HELP', 'NEEDS_SHOPPING', 'SAFETY_REVIEW_REQUIRED']",
                "with": {
                    "query": "{{ inputs.get('collected', {}).get('project_clarify', {}).get('topic', '') }} {{ outputs.get('preferences', '') | truncate(160) }} {{ inputs.user_message | xml_escape | truncate(160) }} kid science project step-by-step instructions safe",
                    "engines": [
                        "brave",
                        "tavily",
                        "duckduckgo"
                    ],
                    "max_results": 8
                },
                "label": "网页研究",
                "skill": "multi-search-engine",
                "label_en": "Web research",
                "depends_on": [
                    "feasibility"
                ],
                "on_failure": "web_research_fallback"
            },
            {
                "id": "web_research_fallback",
                "kind": "llm_chat",
                "with": {
                    "task": "Web research was not available. Extract the project topic, age,\ndeadline, materials, budget, parent availability, location, light,\nweather-sensitive constraints, and safety needs only from the pasted\nrequest. Do not expose tool names, paths, stack traces, connector\nwording, or runtime failures.\n\nRequest:\n{{ inputs.user_message | xml_escape | truncate(3500) }}\n",
                    "system": "You produce a no-web fallback note for child project planning."
                },
                "label": "网页研究兜底",
                "label_en": "Web research fallback"
            },
            {
                "id": "weather_check",
                "kind": "skill_exec",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and ('outdoor' in (inputs.user_message | lower) or 'balcony' in (inputs.user_message | lower) or 'plant' in (inputs.user_message | lower) or 'garden' in (inputs.user_message | lower) or 'park' in (inputs.user_message | lower) or '户外' in inputs.user_message or '阳台' in inputs.user_message or '植物' in inputs.user_message or '豆芽' in inputs.user_message)",
                "with": {
                    "days": 7,
                    "location": "{{ inputs.user_message | xml_escape | truncate(60) }}"
                },
                "label": "天气检查",
                "skill": "weather",
                "label_en": "Weather check",
                "depends_on": [
                    "feasibility",
                    "project_clarify"
                ],
                "on_failure": "weather_check_fallback"
            },
            {
                "id": "weather_check_fallback",
                "kind": "llm_chat",
                "with": {
                    "task": "Live weather was not verified. Continue using only the pasted\nlocation and the user's supplied light\/outdoor\/indoor context. Do not\ninfer forecasts, temperature ranges, rainfall, balcony direction,\nsunshine hours, or season-specific claims. Do not mention tool\nfailures.\n\nRequest:\n{{ inputs.user_message | xml_escape | truncate(2000) }}\n",
                    "system": "You produce a no-live-weather fallback note for child project planning."
                },
                "label": "天气兜底",
                "label_en": "Weather fallback"
            },
            {
                "id": "project_fact_ledger",
                "kind": "llm_chat",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "with": {
                    "task": "Build a strict project fact ledger from the user's request and any\nclarification payload. Durable memory is also a source of facts when\nthe memory output clearly states child profile, prior projects,\npreferences, or parent availability. Ignore memory status prose,\nfile inventory, workspace paths, and runtime\/tool wording; extract\nonly the actual remembered facts.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(3500) }}\n\nClarification:\n{{ inputs.get('collected', {}).get('project_clarify', {}) | tojson | truncate(1200) }}\n\nDurable memory \/ past-project recall:\n{{ outputs.get('recall_past_projects', '') | truncate(1800) }}\n\nWeather result or fallback:\n{{ outputs.get('weather_check', '') | truncate(800) }}\n\nReturn exactly:\nOUTPUT_LANGUAGE: <zh|en|mixed>\nPROVIDED_CHILD_CONTEXT:\n  - <age, ability, child preferences, guardian availability, or UNKNOWN>\nPROVIDED_MEMORY_CONTEXT:\n  - <remembered child profile, prior projects, parent constraints, lessons learned, or none>\nPROVIDED_PROJECT_CONTEXT:\n  - <topic, school deadline\/time window, school output, or UNKNOWN>\nPROVIDED_MATERIALS_BUDGET:\n  - <available materials and budget exactly as supplied, or UNKNOWN>\nPROVIDED_LOCATION_LIGHT:\n  - <location and light exactly as supplied, or UNKNOWN>\nVERIFIED_WEATHER:\n  - <verified forecast if actually present in weather result, else none>\nUNKNOWN_DETAILS:\n  - <exact date, balcony direction, exact weather, exact school format, etc.>\nFORBIDDEN_INFERENCES:\n  - <details that must not appear as facts, e.g. exact calendar date,\n    balcony faces south\/east\/west, temperature range, rain forecast,\n    school rule, allergy, fake measurements, tasting\/eating>\n\nRules:\n- If durable memory says the child is a specific age, likes\/dislikes\n  an activity style, has prior projects, or has parent time limits,\n  put those in PROVIDED_CHILD_CONTEXT and PROVIDED_MEMORY_CONTEXT.\n  Do not mark them UNKNOWN.\n- If the current request asks not to repeat previous projects, list\n  remembered prior projects in PROVIDED_MEMORY_CONTEXT so downstream\n  steps can avoid them explicitly.\n- If the source says only \"two weeks later\", mark exact date UNKNOWN.\n- If the source says only \"half-day sun\", mark orientation and hours\n  UNKNOWN. Preserve \"half-day sun\" exactly.\n- If weather was not actually verified, VERIFIED_WEATHER must be none.\n- Never invent sample measurements, dates, allergies, school rules,\n  or local forecasts.\n",
                    "system": "You extract a strict source-fact ledger for a child project plan. You do not write the plan. You separate user-provided facts, durable memory facts, unknowns, and unsafe or unsupported inferences."
                },
                "label": "项目事实台账",
                "label_en": "Project fact ledger",
                "depends_on": [
                    "preferences",
                    "project_clarify",
                    "recall_past_projects",
                    "weather_check"
                ]
            },
            {
                "id": "deep_research",
                "kind": "skill_exec",
                "when": "outputs.feasibility in ['SAFETY_REVIEW_REQUIRED', 'NEEDS_SHOPPING']",
                "with": {
                    "depth": "standard",
                    "query": "{{ inputs.get('collected', {}).get('project_clarify', {}).get('topic', '') }} safe materials children",
                    "max_rounds": 1
                },
                "label": "深度研究",
                "skill": "deep-research",
                "label_en": "Deep research",
                "depends_on": [
                    "feasibility"
                ]
            },
            {
                "id": "outline_steps",
                "kind": "llm_chat",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "with": {
                    "task": "Build a step-by-step plan grounded in the topic and research.\n\nTopic + context:\n{{ outputs.get('preferences', '') | truncate(500) }}\n{{ inputs.get('collected', {}).get('project_clarify', {}) | tojson }}\nIf clarification fields are empty, extract topic, age, deadline,\nbudget, materials, parent availability, location, and light from the\nuser request:\n{{ inputs.user_message | xml_escape | truncate(2500) }}\n\nPast projects this child has done (avoid repeats; build on prior learning):\n{{ outputs.get('recall_past_projects', '') | truncate(600) }}\n\nWeather (for outdoor projects — pick a good day):\n{{ outputs.get('weather_check', '') | truncate(400) }}\n\nWeb research:\n{{ outputs.get('web_research', '') | truncate(2500) }}\n\nDeep research (only present if research happened):\n{{ outputs.get('deep_research', '') | truncate(2000) }}\n\nDeadline: {{ inputs.get('collected', {}).get('project_clarify', {}).get('deadline_days', 14) }} days.\nAge band: {{ inputs.get('collected', {}).get('project_clarify', {}).get('age_band', 'EARLY_GRADE') }}.\nParent supervision: {{ inputs.get('collected', {}).get('project_clarify', {}).get('parent_supervision', 'LIGHT') }}.\n\nOutput a markdown numbered list. Each step has:\n- Title (one short sentence, kid-readable verb-first)\n- Time estimate (5-20 min)\n- Adult-needed: yes \/ no (be honest)\n- One supportive sentence (\"You'll get to ...\")\n\nDistribute the steps across the available days. If deadline is\ntight, mark which steps to skip or shorten.\n\nLanguage: match preferences (default mixed).\n",
                    "system": "You break a project into kid-sized steps. Each step takes 5-20 minutes for an unhurried child. No step requires reading more than a paragraph. Use concrete, doable verbs."
                },
                "label": "步骤大纲",
                "label_en": "Step outline",
                "depends_on": [
                    "feasibility",
                    "web_research",
                    "recall_past_projects",
                    "weather_check",
                    "project_clarify",
                    "preferences"
                ]
            },
            {
                "id": "material_list",
                "kind": "llm_chat",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "with": {
                    "task": "List materials needed.\n\nStep plan:\n{{ outputs.outline_steps | truncate(2500) }}\n\nBudget band: {{ inputs.get('collected', {}).get('project_clarify', {}).get('budget_band', 'MODEST') }}.\n\nOutput a markdown table with columns:\nitem | quantity | est_cost | shoestring_substitute |\nnotes_if_substitute_changes_outcome.\n\nAdd at the end a bullet line \"Likely you have at home:\" listing\nitems the household probably already owns.\n",
                    "system": "You list materials needed for a kid project. For each material, offer a SHOESTRING substitute the family likely has at home. Always be honest when a substitute genuinely won't work."
                },
                "label": "材料清单",
                "label_en": "Materials list",
                "depends_on": [
                    "outline_steps",
                    "project_clarify",
                    "web_research"
                ]
            },
            {
                "id": "safety_notes",
                "kind": "llm_chat",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "with": {
                    "task": "Surface safety notes specific to this project.\n\nFeasibility: {{ outputs.feasibility }}\nAge band: {{ inputs.get('collected', {}).get('project_clarify', {}).get('age_band', 'EARLY_GRADE') }}.\nStep plan (for reference):\n{{ outputs.outline_steps | truncate(2000) }}\n\nOutput bullet points grouped under:\n## ⚠️ Adult must be present for\n## ✋ Stop and call an adult if\n## 🧪 Materials to handle carefully\n\nEach bullet must reference a specific step from the plan (e.g.\n\"Step 4 (mixing): vinegar can spray — wear safety glasses or\nold sunglasses\").\n\nIf feasibility is STRAIGHTFORWARD and the child is TWEEN+,\nkeep this section short (3-5 bullets). If SAFETY_REVIEW_REQUIRED\nor younger child, be thorough (8-12 bullets).\n\nLanguage: match preferences (default mixed).\n",
                    "system": "You surface safety considerations for a kid project. Be specific (not generic 'be careful'). Tailor to the age band. If feasibility says SAFETY_REVIEW_REQUIRED, the safety section is the most important part of the deliverable."
                },
                "label": "安全提示",
                "label_en": "Safety notes",
                "depends_on": [
                    "feasibility",
                    "outline_steps",
                    "project_clarify"
                ]
            },
            {
                "id": "learning_objectives",
                "kind": "llm_chat",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "with": {
                    "task": "Write the parent-facing learning objectives.\n\nTopic + step plan:\n{{ outputs.get('preferences', '') | truncate(400) }}\n{{ outputs.outline_steps | truncate(2500) }}\n\nOutput:\n## 👀 What your kid will actually learn\n\n3-5 bullets, each one concrete learning outcome grounded in a\nspecific step. Avoid generic outcomes like \"creativity\" or\n\"problem-solving\" — name the specific concept (e.g. \"How an\nacid-base reaction releases CO2 — they'll see the bubbling\nslowdown when vinegar runs out\").\n\n## 🧠 Conversation prompts during\/after\n3 questions the parent can ask the child to deepen the\nlearning. Each question must be open-ended.\n\nLanguage: match preferences (parent-facing — usually adult\nregister).\n",
                    "system": "You write the parent-facing learning-objective section. This is what the GUARDIAN reads to know what their child is actually getting out of the project beyond the artifact."
                },
                "label": "学习目标",
                "label_en": "Learning goals",
                "depends_on": [
                    "outline_steps",
                    "preferences",
                    "project_clarify"
                ]
            },
            {
                "id": "kid_deck",
                "kind": "skill_exec",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences and inputs.get('collected', {}).get('project_clarify', {}).get('parent_supervision', 'LIGHT') == 'HANDS_ON'",
                "with": {
                    "mode": "create",
                    "title": "🛠️ {{ inputs.get('collected', {}).get('project_clarify', {}).get('topic', 'Your project') }}",
                    "slides": [
                        {
                            "body": "{{ outputs.get('preferences', '') | truncate(400) }}",
                            "title": "What we're going to make"
                        },
                        {
                            "body": "{{ outputs.get('material_list', '') | truncate(800) }}",
                            "title": "What you need (materials)"
                        },
                        {
                            "body": "{{ outputs.get('outline_steps', '') | truncate(1200) }}",
                            "title": "Step-by-step plan"
                        },
                        {
                            "body": "{{ outputs.get('safety_notes', '') | truncate(600) }}",
                            "title": "⚠️ Stop and call grown-up if"
                        }
                    ],
                    "output_path": "kid_project_{{ inputs.get('collected', {}).get('project_clarify', {}).get('topic', 'untitled') | slugify }}.pptx"
                },
                "label": "儿童卡片",
                "skill": "pptx",
                "label_en": "Child card",
                "depends_on": [
                    "outline_steps",
                    "material_list",
                    "safety_notes",
                    "project_clarify",
                    "feasibility"
                ]
            },
            {
                "id": "vocab_cards",
                "kind": "llm_chat",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences and ('vocab' in (inputs.user_message | lower) or 'word card' in (inputs.user_message | lower) or 'bilingual' in (inputs.user_message | lower) or '英语' in inputs.user_message or '双语' in inputs.user_message or '单词' in inputs.user_message)",
                "with": {
                    "task": "Produce 6 vocab cards from the project content.\n\nStep plan:\n{{ outputs.outline_steps | truncate(2000) }}\n\nMaterials:\n{{ outputs.material_list | truncate(800) }}\n\nAge band: {{ inputs.get('collected', {}).get('project_clarify', {}).get('age_band', 'EARLY_GRADE') }}.\n\nFor each card:\n- Term (one English word + one Chinese gloss if the language\n  preference is `mixed`)\n- Kid-friendly definition (one sentence)\n- Example sentence from the project (\"In step 3, the vinegar\n  ACID meets the baking soda BASE...\")\n\nOutput as a markdown numbered list.\n",
                    "system": "You produce a small vocabulary card list grounded in the project content. Each card is age-appropriate."
                },
                "label": "词汇卡",
                "label_en": "Vocabulary card",
                "depends_on": [
                    "outline_steps",
                    "material_list",
                    "project_clarify",
                    "feasibility",
                    "preferences"
                ]
            },
            {
                "id": "deliver_project_pack",
                "kind": "llm_chat",
                "with": {
                    "task": "Assemble the final project pack.\n\nIf the project was unsafe (redirect_unsafe ran), output ONLY\nthe content of {{ outputs.get('redirect_unsafe', '') }} verbatim\nand end with PACK_DELIVERED: no_safety_redirect — nothing else.\n\nOtherwise, synthesize a concise, non-duplicative project pack.\ndo not copy intermediate outputs verbatim when that would repeat\nsafety text, parent instructions, or child instructions. Use the\nintermediate outputs as source material and rewrite them into one\npractical final answer.\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(3500) }}\n\nProject fact ledger:\n{{ outputs.get('project_fact_ledger', '') | truncate(2500) }}\n\nDurable memory \/ past-project recall:\n{{ outputs.get('recall_past_projects', '') | truncate(1600) }}\n\nSource-constraint audit:\n- Treat the Project fact ledger as the source of truth. If an\n  intermediate step conflicts with it, ignore the intermediate step.\n- Treat PROVIDED_MEMORY_CONTEXT in the fact ledger as user-relevant\n  durable context. Preserve remembered age, child preferences, parent\n  time limits, prior projects, and lessons learned unless the current\n  request explicitly contradicts them.\n- If the fact ledger marks a detail UNKNOWN, leave it unknown or mark\n  it as an assumption; do not fill it with common sense or current\n  date\/time.\n- Preserve every explicit user constraint before adding suggestions:\n  age, deadline, location, available materials, budget,\n  parent time, light\/weather constraints, school deliverable, and\n  requested output sections.\n- Do not replace user-provided materials with unrelated materials.\n  Prefer the user's materials first, then list substitutes only as\n  backups.\n- Do not invent calendar dates, school dates, temperature readings,\n  weather forecasts, or live conditions. Use day numbers or say the\n  exact date is unknown unless the user supplied one or live data was\n  verified.\n- If the user gives only a relative deadline such as \"two weeks\n  later\", do not convert it into a calendar date. Say \"两周后 \/\n  relative deadline supplied; exact date not provided\" and schedule\n  by Day 1...Day 14.\n- Do not invent balcony direction, temperature ranges, sunshine\n  hours, rain forecasts, school rules, allergies, or local weather.\n  Keep weather\/light claims to what the user supplied plus clearly\n  labelled assumptions.\n- Do not prefill observation tables with fake measurements, fake\n  dates, or predicted heights. For templates, leave measurement cells blank or as placeholders\n  such as \"__ cm\" \/ \"[记录]\".\n- Do not suggest tasting or eating the experiment materials unless\n  the user explicitly asks for an edible-food activity and safety has\n  been reviewed. Observation projects should compare appearance,\n  height, color, firmness by sight\/touch if safe, and drawings.\n- Design a comparison experiment when it fits the project and stays\n  within the user's materials, age, time budget, and safety limits.\n  For observation projects, make the variable, control, and data table\n  clear enough for a school presentation.\n- Prefer a clear comparison design for plant\/observation projects:\n  same seed, cup, water, and paper-towel conditions, with only one\n  changed variable such as light exposure. If materials allow, use\n  2-3 labelled groups; if not, make the single-group observation\n  plan still presentation-ready.\n- \"Printable\" means a clean markdown table, worksheet block, or\n  poster-board layout that the user can print from chat. Do not\n  create or refer to PDFs, HTML files, downloads, attachments,\n  local paths, generated artifacts, or workspace files unless the\n  user explicitly asked for a file\/PDF\/export\/download.\n- If the user asks for a beautiful or visually polished plan, make\n  the inline markdown itself polished: a memorable title, a concise\n  visual theme, color\/palette suggestions, kid-facing labels,\n  a drawing-heavy record sheet, and a parent-ready poster layout.\n- If the user asks to start with remembered constraints, include a\n  section titled exactly \"## Remembered constraints I used\" near the\n  top and list only facts found in PROVIDED_MEMORY_CONTEXT or the\n  current request. Do not invent memory.\n\nLanguage and length:\n- Match the user's language. For Chinese requests, write Simplified\n  Chinese throughout, including headings and child-facing text.\n- For Chinese requests, do not use English section headings such as\n  \"For You (the kid)\" or \"For the Grown-up\".\n- Do not include vocabulary cards unless the user explicitly asked\n  for vocab, word cards, bilingual support, 英语, 双语, or 单词.\n- For straightforward home\/school projects, target 1800-3200 Chinese characters\n  or an equivalent compact English length unless the user asks for a\n  long worksheet.\n\nFor Chinese safe projects, use this structure:\n\n# 🛠️ <Project Title>\n\n## 先说假设\nState the age\/deadline\/material\/weather assumptions and which facts\nare not live-verified.\n\n## 项目设计\nExplain the child-friendly project question, final deliverable, and\n2-3 learning goals in plain language.\n\n## 14 天计划\nGive an actionable schedule. Group days into phases when that is\nclearer than 14 long paragraphs, but preserve deadlines, daily\nobservation habits, parent touchpoints, and what the child does.\n\n## 材料和替代品\nSummarize the materials table and substitutions. Keep shopping and\nhousehold alternatives practical.\n\n## 安全和翻车点\nInclude only the safety points that change behavior: stop-and-call\nconditions, allergy\/toxin\/sharp\/hot\/water\/electricity risks where\nrelevant, and 3 common failure modes with fixes.\n\n## 数据记录和画图\nInclude a simple observation template for date, height, leaf\/color,\nwater, light, and one child-friendly chart idea.\n\n## 天气\/光照调整\nUse live weather only if verified; otherwise state \"live weather not\nverified \/ 实时天气未核验\" and give safe indoor\/low-light alternatives.\n\n## 最后展示怎么讲\nProvide a 60-second child script and 3 likely teacher questions with\nsimple answers.\n\n## 家长每晚 20 分钟\nSummarize what the adult checks each night and which steps require\nhands-on help.\n\n## 还缺哪些实时信息\nList only genuinely missing information that would improve the plan,\nwithout asking the user to confirm before using the current answer.\n\nFor English safe projects, use equivalent English headings:\nremembered constraints used when requested, known facts and\nassumptions, project design, 14-day schedule,\nmaterials\/substitutes, safety\/failure modes, printable record\nsheet, poster-board layout, simple science explanation,\nWeather \/ light adjustment, adult 20-minute check, and missing live\ninfo. If the user asked for a visually polished plan, include a\nshort \"visual theme\" subsection.\n\nEnd with a single line:\nPACK_DELIVERED: {{ outputs.feasibility }}\n",
                    "system": "You assemble the final project pack the user will read. Return the complete deliverable inline in chat. Do not create, save, export, attach, or point primarily to an artifact unless the user explicitly asked for a file export with words like PDF, file, export, attachment, or download. Treat requests for a printable worksheet, printable record sheet, or poster layout as print-ready markdown included inline. Never mention workflow, meta-skill, tool names, connector failures, workspace paths, or runtime details."
                },
                "label": "项目包交付",
                "label_en": "Project package delivery",
                "depends_on": [
                    "preferences",
                    "feasibility",
                    "redirect_unsafe",
                    "outline_steps",
                    "material_list",
                    "safety_notes",
                    "learning_objectives",
                    "vocab_cards",
                    "kid_deck",
                    "recall_past_projects",
                    "weather_check",
                    "project_fact_ledger",
                    "project_clarify"
                ]
            },
            {
                "id": "project_pack_audit",
                "kind": "llm_chat",
                "with": {
                    "task": "Rewrite the draft below into the final user-facing project pack.\nPreserve useful content, but enforce the fact ledger strictly. If\nthe draft is only JSON, artifact metadata, download references,\nprocess commentary, or otherwise not a complete user-facing answer,\nrebuild the final project pack from the fact ledger and intermediate\nsource sections below.\n\nUnsafe redirect source:\n{{ outputs.get('redirect_unsafe', '') | truncate(1800) }}\n\nProject fact ledger:\n{{ outputs.get('project_fact_ledger', '') | truncate(2500) }}\n\nDurable memory \/ past-project recall:\n{{ outputs.get('recall_past_projects', '') | truncate(1600) }}\n\nStep plan source:\n{{ outputs.get('outline_steps', '') | truncate(2600) }}\n\nMaterials source:\n{{ outputs.get('material_list', '') | truncate(1600) }}\n\nSafety source:\n{{ outputs.get('safety_notes', '') | truncate(1600) }}\n\nLearning source:\n{{ outputs.get('learning_objectives', '') | truncate(1600) }}\n\nWeather\/light source:\n{{ outputs.get('weather_check', '') | truncate(900) }}\n\nDraft project pack:\n{{ outputs.get('deliver_project_pack', '') | truncate(8000) }}\n\nRequired audit rules:\n- Return markdown only. Never return JSON, artifact metadata, file\n  paths, download links, or attachment notes.\n- If feasibility is INAPPROPRIATE, PROJECT_SAFE is no, or the unsafe\n  redirect source is non-empty, return the unsafe redirect source as\n  the final answer. Preserve its refusal and all safe alternative\n  project ideas. Do not rebuild a normal project pack. End with\n  PACK_DELIVERED: no_safety_redirect.\n- If the draft contains JSON keys such as \"text\", \"artifacts\",\n  \"artifact_ref\", \"download_url\", \"mime\", \"sha256\", \"session_id\",\n  \"created_at\", or \"store\", discard those metadata fields and write\n  a normal markdown answer instead.\n- Remove leading process commentary such as \"perfect match\", \"let me\n  run it\", \"I will run\", \"workflow\", \"meta-skill\", or any similar\n  explanation of how the answer was produced. The first non-empty\n  line must be the user-facing project title, unless the user\n  explicitly requested a different first heading such as\n  \"Remembered constraints I used\".\n- Preserve the user's language. If the request is English, write\n  English-only prose and English headings. If the request is Chinese,\n  write Simplified Chinese throughout.\n- Remove exact calendar dates, weekdays, months, or current-year references\n  unless the user explicitly provided those exact dates. If the user\n  gave only a relative deadline, use Day 1...Day 14 and say exact\n  date not provided.\n- Remove invented balcony direction, temperature ranges, rain forecasts,\n  sunshine hours, school rules, allergies, and local weather claims\n  unless they appear in the fact ledger as verified or user-provided.\n- Remove fake sample measurements, fake dates, and predicted heights.\n  Observation tables must leave measurement cells blank or as\n  placeholders like \"__ cm\" \/ \"[记录]\".\n- Remove tasting\/eating suggestions. For observation projects, compare\n  height, color, firmness by safe touch if appropriate, drawings, and\n  notes.\n- Keep every explicit user constraint from the fact ledger: child age,\n  relative deadline, location, available materials, budget, parent\n  time, light constraint, and requested sections.\n- Keep every clear durable-memory constraint from the fact ledger:\n  remembered age, child preferences, writing tolerance, parent\n  availability, prior projects, and lessons learned. Do not rewrite\n  those fields as UNKNOWN when they appear in PROVIDED_MEMORY_CONTEXT.\n- If the request asks not to repeat previous projects, explicitly\n  avoid or name the prior projects as non-options in one concise\n  sentence.\n- If the user asks to use remembered facts, include a\n  \"## Remembered constraints I used\" section with the remembered\n  age, preferences, writing tolerance, parent time limit, prior\n  projects, and lessons learned when those facts appear in\n  PROVIDED_MEMORY_CONTEXT. Do not say no memory exists when\n  PROVIDED_MEMORY_CONTEXT contains facts.\n- Prefer a clear comparison design when it fits: same seed, cup,\n  water, and paper-towel conditions, with only light exposure changed.\n- Treat \"printable record sheet\" and \"poster board layout\" as inline\n  deliverables unless the user explicitly asked for PDF\/file\/export.\n  Include a clean markdown worksheet\/table with blank boxes,\n  checkboxes, or placeholders; do not claim that a PDF, HTML file,\n  local file, download, or artifact was generated.\n- For visually polished project packs, make the markdown itself\n  beautiful and school-ready: memorable title, visual theme or\n  palette, kid-facing labels, drawing-heavy worksheet, and a poster\n  layout the parent can recreate.\n- Keep the answer compact and immediately usable: target 2500-3600 Chinese characters\n  for Chinese requests. Avoid long daily tables when grouped phases\n  are clearer.\n\nOutput structure for Chinese requests:\n# 🛠️ <title>\n## 先说清楚哪些是已知、哪些未知\n## 项目设计\n## 14 天计划\n## 材料和替代品\n## 安全和翻车点\n## 数据记录和画图\n## 天气\/光照调整\n## 最后展示怎么讲\n## 家长每晚 20 分钟\n## 还缺哪些实时信息\n\nFor English requests, use equivalent English headings only:\n# <Project title>\n## Remembered constraints I used\n## Known facts and assumptions\n## Project design\n## 14-day plan\n## Materials and substitutes\n## Safety and failure modes\n## Printable record sheet\n## Poster-board layout\n## Simple science explanation\n## Weather \/ light adjustment\n## Adult 20-minute check\n## Missing live information\n\nEnd with:\nPACK_DELIVERED: {{ outputs.feasibility }}",
                    "system": "You are the final quality gate for a child project plan. Return only the cleaned final answer that the user should read. Do not explain the audit. Do not mention workflow, meta-skill, tool names, connector failures, workspace paths, or runtime details."
                },
                "label": "项目包审稿",
                "label_en": "Project package review",
                "depends_on": [
                    "deliver_project_pack",
                    "redirect_unsafe",
                    "project_fact_ledger",
                    "recall_past_projects",
                    "outline_steps",
                    "material_list",
                    "safety_notes",
                    "learning_objectives",
                    "weather_check",
                    "feasibility",
                    "preferences"
                ]
            },
            {
                "id": "store_project",
                "kind": "tool_call",
                "tool": "memory_save",
                "when": "outputs.feasibility != 'INAPPROPRIATE' and 'PROJECT_SAFE: yes' in outputs.preferences",
                "label": "存储项目",
                "label_en": "Store project",
                "tool_args": {
                    "mode": "append",
                    "path": "memory\/meta-kid-projects.md",
                    "content": "## Kid project run\n\nPreferences:\n{{ outputs.get('preferences', '') | truncate(1200) }}\n\nFact ledger:\n{{ outputs.get('project_fact_ledger', '') | truncate(1800) }}\n\nFinal project pack excerpt:\n{{ outputs.get('project_pack_audit', '') | truncate(2400) }}"
                },
                "depends_on": [
                    "project_pack_audit",
                    "project_clarify",
                    "feasibility"
                ],
                "tool_allowlist": [
                    "memory_save"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when a child or their guardian wants to plan a school project, science fair entry, hobby kit, or kid-sized creative venture (volcano model, bug-watching YouTube channel, magnet maze, model rocket). The skill assesses feasibility against the child's age band, builds an age-appropriate step plan, lists materials with budget substitutes, surfaces safety considerations, and produces a parent-facing learning-objective summary so the guardian can supervise meaningfully. Refuses inappropriate or unsafe projects.",
    "policy_tags": [
        "child-safety",
        "age-appropriate"
    ],
    "eval_prompts": [
        {
            "name": "kid-project-baseline",
            "prompt": "Plan a safe age-appropriate science fair project for an elementary-school child with a small budget.",
            "rubric": [
                "Feasibility verdict",
                "Step-by-step plan",
                "Materials and substitutions",
                "Safety notes",
                "Guardian learning objectives"
            ]
        }
    ],
    "meta_priority": 60,
    "final_text_mode": "step:project_pack_audit",
    "output_contract": {
        "artifacts": [
            {
                "name": "project_pack",
                "required": false
            }
        ],
        "unverified": [
            "Local material availability and school-specific rubric details."
        ],
        "assumptions": [
            "Age band and supervision level may be defaulted when absent."
        ],
        "required_sections": [
            "Feasibility verdict",
            "Step-by-step plan",
            "Materials and substitutions",
            "Safety notes",
            "Guardian learning objectives"
        ],
        "append_to_final_text": false
    },
    "preference_keys": [
        "preferred_language",
        "guardian_supervision_level"
    ],
    "request_template": {
        "fields": [
            {
                "name": "project_topic",
                "label_en": "Project topic",
                "label_zh": "项目主题",
                "required": true
            },
            {
                "name": "age_band",
                "default": "early grade",
                "label_en": "Age band",
                "label_zh": "年龄段",
                "required": false,
                "default_en": "early grade",
                "default_zh": "小学低年级"
            },
            {
                "name": "deadline_days",
                "label_en": "Days until due",
                "label_zh": "截止天数",
                "required": false
            },
            {
                "name": "budget_or_material_constraints",
                "label_en": "Budget or material constraints",
                "label_zh": "预算或材料限制",
                "required": false
            },
            {
                "name": "audience",
                "default": "child plus guardian",
                "label_en": "Audience",
                "label_zh": "受众",
                "required": false,
                "default_en": "child plus guardian",
                "default_zh": "孩子和监护人"
            },
            {
                "name": "language",
                "default": "match the user's language",
                "label_en": "Output language",
                "label_zh": "输出语言",
                "required": false,
                "default_en": "match the user's language",
                "default_zh": "跟随用户语言"
            }
        ],
        "outcome": "Age-appropriate project plan with materials, safety notes, and guardian guidance.",
        "outcome_en": "Age-appropriate project plan with materials, safety notes, and guardian guidance.",
        "outcome_zh": "适合孩子年龄的项目计划，包含材料、安全提示和家长指导。",
        "assumptions": [
            "Refuse unsafe or age-inappropriate projects.",
            "Default to modest materials and light guardian supervision when unspecified."
        ],
        "assumptions_en": [
            "Refuse unsafe or age-inappropriate projects.",
            "Default to modest materials and light guardian supervision when unspecified."
        ],
        "assumptions_zh": [
            "拒绝不安全或不适合年龄的项目。",
            "未说明时，默认使用适度材料并安排轻量家长监督。"
        ]
    }
}

{
    "kind": "meta",
    "name": "meta-paper-write",
    "always": false,
    "metadata": {
        "platform": {
            "requires": {
                "bins": [
                    "xelatex",
                    "bibtex"
                ]
            }
        },
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "filesystem-write",
                "process-control"
            ]
        }
    },
    "triggers": [
        "draft a paper",
        "write a research paper",
        "academic manuscript",
        "research manuscript",
        "latex manuscript",
        "long-form paper",
        "写篇论文",
        "写一篇论文",
        "撰写论文"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "paper_collect",
                "kind": "llm_chat",
                "with": {
                    "task": "Extract a structured paper brief from the original user request.\nDo NOT ask a question in this step. Instead, mark\nNEEDS_CLARIFICATION: yes when any required field is missing,\nambiguous, or only guessable. The next paper_clarify step will\nask the user for missing information.\n\nMode defaults:\n- Use COMPACT_SKELETON by default for ordinary \"write\/draft a\n  paper\" requests. This is the fast path and still produces a\n  coherent LaTeX-ready draft with citations and a compiled PDF.\n- Use FULL_MANUSCRIPT only when the user explicitly asks for a full\n  manuscript, long-form paper, publication-ready paper, PDF, LaTeX\n  manuscript, section-by-section drafting, or gives a target of 8+\n  pages.\n- Use COMPACT_SKELETON when the user explicitly asks for a short\n  skeleton, outline, compact draft, or does not specify length.\n- Use REPAIR_EXISTING only when the user provides or references an\n  existing manuscript to fix.\n- Use COMPILE_ONLY only when the user explicitly asks only to compile\n  an existing LaTeX manuscript.\n\nClarification policy:\n- Required field: topic.\n- Infer language from the user request whenever possible. For an\n  English request, set LANGUAGE: en. For a Chinese request, set\n  LANGUAGE: zh.\n- If target pages are missing, use TARGET_PAGES: 4 for\n  COMPACT_SKELETON and 10 for FULL_MANUSCRIPT.\n- If audience is missing, use AUDIENCE: academic.\n- Set NEEDS_CLARIFICATION: yes only when the topic is missing or\n  the request explicitly asks to be interviewed before drafting.\n- Do not set NEEDS_CLARIFICATION: yes for missing paper_mode,\n  language, target_pages, citation_target, or audience; apply the\n  defaults above instead.\n- If clarification is required, write CLARIFY_QUESTION in the same\n  language as the original request. For English requests, the\n  question must be English.\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(1400) }}\n\nReturn exactly:\nTOPIC: <paper topic, or MISSING_TOPIC>\nPAPER_MODE: <FULL_MANUSCRIPT|COMPACT_SKELETON|REPAIR_EXISTING|COMPILE_ONLY>\nLANGUAGE: <en|zh|ja|other>\nTARGET_PAGES: <integer 1-50, or MISSING_TARGET_PAGES>\nAUDIENCE: <academic|technical|business|general>\nCITATION_TARGET: <integer if user explicitly requested one, otherwise AUTO>\nNEEDS_CLARIFICATION: <yes|no>\nMISSING_FIELDS:\n  - <field name, or none>\nCLARIFY_QUESTION: <single concise question in the same language as the original request if NEEDS_CLARIFICATION is yes, otherwise none>\nASSUMPTIONS:\n  - <assumption or none>\n",
                    "system": "You extract paper requirements and decide whether clarification is required."
                },
                "label": "论文收集",
                "label_en": "Paper intake"
            },
            {
                "id": "paper_clarify",
                "kind": "user_input",
                "when": "'NEEDS_CLARIFICATION: yes' in outputs.paper_collect",
                "label": "论文澄清",
                "clarify": {
                    "mode": "form",
                    "intro": "{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}\n论文信息还不完整。请补齐下面字段；除非你选择完整论文，我会优先使用更快的草稿模式。\n{% else %}\nSome paper details are missing. Please fill in the fields below; I will draft with the fastest suitable mode unless you choose a full manuscript.\n{% endif %}\n",
                    "fields": [
                        {
                            "name": "topic",
                            "type": "string",
                            "prompt": "{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}论文主题{% else %}Paper topic{% endif %}",
                            "required": true,
                            "max_chars": 200,
                            "prompt_en": "Paper topic",
                            "prompt_zh": "论文主题"
                        },
                        {
                            "name": "paper_mode",
                            "type": "enum",
                            "prompt": "{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}类型（默认 COMPACT_SKELETON = 更快草稿；选择 FULL_MANUSCRIPT 生成完整论文 + PDF）{% else %}Mode (default COMPACT_SKELETON = faster draft; choose FULL_MANUSCRIPT for full paper + PDF){% endif %}",
                            "choices": [
                                "FULL_MANUSCRIPT",
                                "COMPACT_SKELETON",
                                "REPAIR_EXISTING",
                                "COMPILE_ONLY"
                            ],
                            "default": "COMPACT_SKELETON",
                            "prompt_en": "Mode (default COMPACT_SKELETON = faster draft; choose FULL_MANUSCRIPT for full paper + PDF)",
                            "prompt_zh": "类型（默认 COMPACT_SKELETON = 更快草稿；选择 FULL_MANUSCRIPT 生成完整论文 + PDF）"
                        },
                        {
                            "name": "language",
                            "type": "enum",
                            "prompt": "{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}语言{% else %}Language{% endif %}",
                            "choices": [
                                "en",
                                "zh",
                                "ja",
                                "other"
                            ],
                            "required": true,
                            "prompt_en": "Language",
                            "prompt_zh": "语言"
                        },
                        {
                            "max": 50,
                            "min": 1,
                            "name": "target_length_pages",
                            "type": "int",
                            "prompt": "{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}目标页数（1-50）{% else %}Target pages (1-50){% endif %}",
                            "default": 4,
                            "prompt_en": "Target pages (1-50)",
                            "prompt_zh": "目标页数（1-50）"
                        },
                        {
                            "name": "audience",
                            "type": "enum",
                            "prompt": "{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}受众{% else %}Audience{% endif %}",
                            "choices": [
                                "academic",
                                "technical",
                                "business",
                                "general"
                            ],
                            "default": "academic",
                            "prompt_en": "Audience",
                            "prompt_zh": "受众"
                        }
                    ],
                    "intro_en": "Some paper details are missing. Please fill in the fields below; I will draft with the fastest suitable mode unless you choose a full manuscript.",
                    "intro_zh": "论文信息还不完整。请补齐下面字段；除非你选择完整论文，我会优先使用更快的草稿模式。",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "算了",
                        "取消",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "label_en": "Paper clarification",
                "depends_on": [
                    "paper_collect"
                ]
            },
            {
                "id": "paper_contract",
                "kind": "llm_chat",
                "with": {
                    "task": "Build the final paper contract. Prefer explicit clarification\nanswers over the first-pass extraction. If clarification is empty,\nuse only confidently extracted values. Do not invent missing topic.\n\nFirst-pass extraction:\n{{ outputs.paper_collect | truncate(1200) }}\n\nClarification answers (may be empty when not needed):\n{{ inputs.get('collected', {}).get('paper_clarify', {}) | tojson }}\n\nOriginal user request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nReturn exactly:\nTOPIC: <resolved topic>\nPAPER_MODE: <FULL_MANUSCRIPT|COMPACT_SKELETON|REPAIR_EXISTING|COMPILE_ONLY>\nLANGUAGE: <en|zh|ja|other>\nTARGET_PAGES: <integer 1-50>\nAUDIENCE: <academic|technical|business|general>\nCITATION_TARGET: <integer if explicitly requested, otherwise AUTO>\nPDF_REQUIRED: yes\nASSUMPTIONS:\n  - <assumption or none>\n",
                    "system": "You merge extracted paper requirements and clarification answers into the final paper contract."
                },
                "label": "论文契约",
                "label_en": "Paper contract",
                "depends_on": [
                    "paper_collect",
                    "paper_clarify"
                ]
            },
            {
                "id": "paper_preferences",
                "kind": "llm_chat",
                "with": {
                    "task": "Expand the extracted paper facts into a full planning contract.\n\nExtracted paper contract (DO NOT override these):\n{{ outputs.paper_contract | truncate(1200) }}\n\nOriginal user request (context only, do NOT override confirmed facts):\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nReturn exactly:\nPAPER_MODE: <copy PAPER_MODE from extracted contract verbatim>\nMODE: DIRECT\nTOPIC: <copy TOPIC from extracted contract verbatim>\nAUDIENCE: <copy AUDIENCE from extracted contract verbatim>\nVENUE_STYLE: <generic research paper or inferred venue>\nLANGUAGE: <copy LANGUAGE from extracted contract verbatim — use the exact enum value, do not translate>\nTARGET_LENGTH: <copy TARGET_PAGES from extracted contract verbatim> compiled pages unless the user requested a different unit\nCITATION_TARGET: <copy explicit citation target, otherwise derive from target length, source availability, audience, and venue style>\nLENGTH_STRATEGY: <section-level page\/word allocation based on TARGET_LENGTH and user intent>\nCITATION_STRATEGY: <how many sources to use per major section and why>\nCITATION_STYLE: BibTeX cite keys, LaTeX \\cite{...}\nASSUMPTIONS:\n  - <assumption>\n",
                    "system": "You expand extracted paper requirements into a structured planning contract."
                },
                "label": "论文偏好",
                "label_en": "Paper preferences",
                "depends_on": [
                    "paper_contract"
                ]
            },
            {
                "id": "search_query_translation",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "with": {
                    "task": "Translate the user-confirmed paper topic into one concise\nEnglish academic search query optimised for arXiv \/ ACL\nAnthology \/ ACM DL \/ OpenReview \/ IEEE \/ Nature \/ Science.\n\nStrict rules:\n- Output ONLY the English query text on a single line.\n- Do NOT include preambles, labels (no \"Query:\", \"Translation:\"),\n  quotes, the word \"search\", boolean operators, site filters,\n  or the year — those are appended downstream by the runtime.\n- Keep it ≤ 12 words; prefer the canonical English term for any\n  non-English research area (e.g. 检索增强生成 → retrieval-augmented\n  generation; 大模型对齐 → large language model alignment).\n- If the topic is already in English, return it unchanged\n  (clean up only obvious typos \/ extraneous words).\n\nTopic (may be Chinese, Japanese, or English):\nTOPIC: {{ outputs.paper_contract | truncate(1200) }}, MODE: {{ outputs.paper_contract | truncate(400) }}, PAGES: {{ outputs.paper_contract | truncate(400) }}\n",
                    "system": "You translate paper topics into concise English academic search queries. Output only the query text."
                },
                "label": "检索翻译",
                "label_en": "Search translation",
                "depends_on": [
                    "paper_contract"
                ]
            },
            {
                "id": "search_papers",
                "kind": "skill_exec",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "with": {
                    "query": "{{ outputs.search_query_translation | xml_escape | truncate(200) }} (site:arxiv.org OR site:aclanthology.org OR site:dl.acm.org OR site:openreview.net OR site:ieee.org OR site:nature.com OR site:science.org)",
                    "engines": [
                        "brave",
                        "duckduckgo",
                        "tavily"
                    ],
                    "max_results": 20
                },
                "label": "论文检索",
                "skill": "multi-search-engine",
                "label_en": "Paper search",
                "depends_on": [
                    "paper_preferences",
                    "search_query_translation"
                ]
            },
            {
                "id": "refbib",
                "kind": "skill_exec",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "with": {
                    "search_results": "{{ outputs.search_papers | truncate(8000) }}"
                },
                "label": "参考文献",
                "skill": "paper-refbib-stub",
                "label_en": "References",
                "depends_on": [
                    "search_papers"
                ]
            },
            {
                "id": "source_pack",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "with": {
                    "task": "Build a source pack for a paper draft. Prefer primary papers,\nofficial documentation, surveys, and reputable technical reports.\nKeep enough usable references to satisfy CITATION_TARGET and\nCITATION_STRATEGY from paper_preferences when the search results\nallow it. If fewer credible references are available than the\nrequested\/derived target, keep all credible references and state the\ngap.\n\nPaper preferences:\n{{ outputs.paper_preferences | truncate(2000) }}\n\nSearch results:\n{{ outputs.search_papers | truncate(8000) }}\n\nBibliography:\n{{ outputs.refbib | truncate(8000) }}\n\nReturn:\nSOURCE_PACK:\nPRIMARY_REFERENCES:\n  - refN | title | supported claim\nCOVERAGE_GAPS:\n  - <gap or none>\n",
                    "system": "You curate paper sources and enforce citation coverage."
                },
                "label": "来源包",
                "label_en": "Source pack",
                "depends_on": [
                    "search_papers",
                    "refbib"
                ]
            },
            {
                "id": "experiment_design",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract",
                "with": {
                    "task": "Design the experiments and supporting figures\/tables for this\npaper. The design must be tight enough that downstream LaTeX\ngeneration can render placeholder figure\/table environments\nstraight from your output.\n\nPaper facts:\nTOPIC: {{ outputs.paper_contract | truncate(1200) }}, MODE: {{ outputs.paper_contract | truncate(400) }}, PAGES: {{ outputs.paper_contract | truncate(400) }}\n\nPreferences:\n{{ outputs.paper_preferences | truncate(2000) }}\n\nSource pack (cite keys must come from here):\n{{ outputs.source_pack | truncate(6000) }}\n\nProvisioning rules (you decide the actual count within these):\n- target ≤8 pages    → 1–2 figures, 0–1 tables\n- target 9–14 pages  → 2–4 figures, 1–2 tables\n- target 15–24 pages → 4–6 figures, 2–3 tables\n- target ≥25 pages   → 6–10 figures, 3–5 tables\nEvery figure\/table MUST trace to a research question or an\nanalysis dimension. Do not invent purely decorative figures.\n\nReply with EXACTLY this structure (verbatim section headers, no\nmarkdown fences):\n\nRESEARCH_QUESTIONS:\n  - id: RQ1\n    question: <one sentence>\n  - id: RQ2\n    question: <one sentence>\n  - id: RQ3\n    question: <one sentence>\n\nHYPOTHESES:\n  - id: H1; supports: RQ1; statement: <one sentence>\n  - id: H2; supports: RQ2; statement: <one sentence>\n\nVARIABLES:\n  independent: <list>\n  dependent: <list>\n  controlled: <list>\n\nDATASETS:\n  - name; size; split; license\/source; rationale\n\nBASELINES:\n  - name; rationale; cite_key (from source_pack); ablation_relationship\n\nMETRICS:\n  - name; definition; supports: RQ#\n\nFIGURE_PLAN:\n  - id: fig1\n    type: <line|bar|scatter|heatmap|violin|timeline|cdf|box|matrix>\n    x_axis: <semantic + unit>\n    y_axis: <semantic + unit>\n    comparison_groups: <list>\n    supports: <RQ#|H#>\n    caption_hint: <short, factual>\n  - id: fig2\n    ... (repeat per provisioning rules)\n\nTABLE_PLAN:\n  - id: tab1\n    columns: <list of column headers>\n    rows_shape: <e.g. \"one row per baseline + ours + 2 ablations\">\n    supports: <RQ#|H#>\n    caption_hint: <short, factual>\n  - id: tab2\n    ... (repeat per provisioning rules)\n\nANALYSIS_DIMENSIONS:\n  - dimension: performance; figures: [fig1]; tables: [tab1]; coverage_note: <why this matters>\n  - dimension: ablation; figures: [fig2]; tables: [tab2]; coverage_note: <...>\n  - dimension: sensitivity_or_robustness; figures: [...]; tables: [...]; coverage_note: <...>\n  - dimension: efficiency; figures: [...]; tables: [...]; coverage_note: <...>\n  - dimension: failure_analysis_or_qualitative; figures: [...]; tables: [...]; coverage_note: <...>\n\nStrict rules:\n- Every figure\/table id appears in at least one ANALYSIS_DIMENSIONS row.\n- Every RESEARCH_QUESTION is supported by ≥1 figure AND\/OR ≥1 table.\n- cite_key fields must reference IDs that exist in source_pack;\n  do not invent new ref keys here.\n",
                    "system": "You design rigorous, falsifiable experiments. You also decide how many figures and tables the paper needs based on the target page budget, the research questions, and the analysis dimensions — do not over- or under-provision."
                },
                "label": "实验设计",
                "label_en": "Experiment design",
                "depends_on": [
                    "paper_preferences",
                    "source_pack"
                ]
            },
            {
                "id": "figure_placeholders",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract",
                "with": {
                    "task": "For EACH figure listed in FIGURE_PLAN below, emit one LaTeX\n``figure`` environment. Use ``\\fbox{\\parbox{0.8\\linewidth}{...}}``\nas the placeholder body — DO NOT use ``\\includegraphics``\nbecause no PDFs exist yet.\n\nBody of each placeholder MUST list:\n  * the figure's id (fig1, fig2, …)\n  * the chart type\n  * x_axis \/ y_axis labels with units\n  * comparison_groups\n  * RQ\/H it supports\n\nCaption must come from caption_hint verbatim (escape LaTeX\nspecials). Label MUST be ``\\label{fig:<id>}`` so analysis_outline\nand final_manuscript_package can ``\\ref{fig:<id>}`` them.\n\nExperiment design:\n{{ outputs.experiment_design | truncate(8000) }}\n\nReply with ONLY the concatenated LaTeX figure environments,\none per FIGURE_PLAN entry, separated by a blank line. No\npreamble, no markdown, no commentary. Wrap the entire block\nbetween sentinel comments so downstream sanitizer can locate\nit:\n\n% BEGIN_FIGURE_PLACEHOLDERS\n\\begin{figure}[t]\n  \\centering\n  \\fbox{\\parbox{0.8\\linewidth}{\\centering\\vspace{1em}\n    \\textbf{[Placeholder] fig1: line plot}\\\\\n    x: training step (1k iter); y: validation accuracy (\\%)\\\\\n    groups: ours \/ baseline-A \/ baseline-B\\\\\n    supports: RQ1\n    \\vspace{1em}}}\n  \\caption{<caption_hint>}\n  \\label{fig:fig1}\n\\end{figure}\n\n\\begin{figure}[t]\n  ... (repeat per FIGURE_PLAN entry)\n\\end{figure}\n% END_FIGURE_PLACEHOLDERS\n",
                    "system": "You render LaTeX placeholder figure environments from a structured figure plan. Output is pure LaTeX, ready to inline into a manuscript."
                },
                "label": "图占位",
                "label_en": "Figure placeholders",
                "depends_on": [
                    "experiment_design"
                ]
            },
            {
                "id": "table_placeholders",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract",
                "with": {
                    "task": "For EACH table listed in TABLE_PLAN below, emit one LaTeX\n``table`` environment with a ``tabular`` body. Use ``---`` or\n``<TBD>`` for cells (DO NOT fabricate numbers). Every non-label data cell MUST be a placeholder;\ntable headers and row labels may be concrete, but metric values, percentages,\ncounts, scores, latency, costs, and confidence intervals must\nremain ``---`` or ``<TBD>`` until real experiments are supplied.\nUse booktabs (``\\toprule``, ``\\midrule``, ``\\bottomrule``) for\nclean spacing.\n\nHeader row comes from TABLE_PLAN columns; row labels come from\nrows_shape (expand the shape into concrete row names like\n\"Baseline-A\", \"Baseline-B\", \"Ours\", \"Ours w\/o module X\", …).\nCaption is caption_hint verbatim. Label MUST be\n``\\label{tab:<id>}``.\n\nExperiment design:\n{{ outputs.experiment_design | truncate(8000) }}\n\nReply with ONLY the concatenated LaTeX table environments,\none per TABLE_PLAN entry, between sentinel comments:\n\n% BEGIN_TABLE_PLACEHOLDERS\n\\begin{table}[t]\n  \\centering\n  \\begin{tabular}{lccc}\n    \\toprule\n    Method & Acc & F1 & Latency \\\\\n    \\midrule\n    Baseline-A & --- & --- & --- \\\\\n    Baseline-B & --- & --- & --- \\\\\n    Ours       & --- & --- & --- \\\\\n    \\bottomrule\n  \\end{tabular}\n  \\caption{<caption_hint>}\n  \\label{tab:tab1}\n\\end{table}\n... (repeat per TABLE_PLAN entry)\n% END_TABLE_PLACEHOLDERS\n",
                    "system": "You render LaTeX placeholder table environments from a structured table plan. Output is pure LaTeX, ready to inline into a manuscript."
                },
                "label": "表占位",
                "label_en": "Table placeholders",
                "depends_on": [
                    "experiment_design"
                ]
            },
            {
                "id": "analysis_outline",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract",
                "with": {
                    "task": "Produce the Analysis chapter outline. Each subsection must\n``\\ref{fig:...}`` or ``\\ref{tab:...}`` AT LEAST ONE artefact\nyou actually have (do not reference figures\/tables that don't\nexist in the placeholders below). Cover every ANALYSIS_DIMENSION\nfrom experiment_design.\n\nExperiment design:\n{{ outputs.experiment_design | truncate(8000) }}\n\nFigure placeholders (label IDs you may \\ref):\n{{ outputs.figure_placeholders | truncate(3000) }}\n\nTable placeholders (label IDs you may \\ref):\n{{ outputs.table_placeholders | truncate(3000) }}\n\nPAPER_MODE depth control:\n- FULL_MANUSCRIPT: 1 subsection per analysis dimension; each\n  with potential_findings (3 bullets) + threats_to_validity\n  (1–2 bullets).\n- COMPACT_SKELETON: 1 subsection per dimension; potential_findings\n  (1 bullet); skip threats_to_validity.\n\nReply in this exact shape between sentinels:\n\n% BEGIN_ANALYSIS_OUTLINE\n\\subsection{Performance}\n\\label{sec:analysis-performance}\nReferences: \\ref{fig:fig1}, \\ref{tab:tab1}.\nPotential findings:\n\\begin{itemize}\n  \\item ...\n\\end{itemize}\nThreats to validity:\n\\begin{itemize}\n  \\item ...\n\\end{itemize}\n\n\\subsection{Ablation}\n... (repeat per ANALYSIS_DIMENSION)\n% END_ANALYSIS_OUTLINE\n",
                    "system": "You design analysis-chapter outlines that bind every figure\/table to a claim and an analysis dimension."
                },
                "label": "分析大纲",
                "label_en": "Analysis outline",
                "depends_on": [
                    "experiment_design",
                    "figure_placeholders",
                    "table_placeholders"
                ]
            },
            {
                "id": "outline",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "with": {
                    "task": "Create a paper outline matching TARGET_PAGES from paper_preferences\nresearch-paper outline with enough section depth for a substantial\nmanuscript. Every section must name planned cite keys from the\nbibliography. Tie the Method section to experiment_design's\nvariables\/datasets\/baselines and the Results section to the\nfigure\/table plan (by id).\n\nPaper preferences:\n{{ outputs.paper_preferences | truncate(2000) }}\n\nSource pack:\n{{ outputs.source_pack | truncate(6000) }}\n\nExperiment design:\n{{ outputs.experiment_design | truncate(6000) }}\n\nCite keys hint:\n{{ outputs.refbib | truncate(8000) }}\n",
                    "system": "You design long-form LaTeX paper outlines with citation plans."
                },
                "label": "大纲",
                "label_en": "Outline",
                "depends_on": [
                    "source_pack",
                    "experiment_design"
                ]
            },
            {
                "id": "citation_plan",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "with": {
                    "task": "Build a citation plan that follows CITATION_TARGET and\nCITATION_STRATEGY from paper_preferences. If the user did not give\nan explicit citation count, derive a target from target length,\nsource availability, audience, and venue style instead of using a\nfixed number. Use only keys that appear in the BibTeX below (every\nkey must be present verbatim — verify by string match before you\nwrite it). Attach citations to claims, not paragraphs in bulk.\n\nTopic and mode:\nTOPIC: {{ outputs.paper_contract | truncate(1200) }}, MODE: {{ outputs.paper_contract | truncate(400) }}, PAGES: {{ outputs.paper_contract | truncate(400) }}\n\nOutline:\n{{ outputs.outline | truncate(6000) }}\n\nSource pack:\n{{ outputs.source_pack | truncate(8000) }}\n\nBibliography (authoritative — cite keys MUST come from here):\n{{ outputs.refbib | truncate(8000) }}\n\nPaper preferences (authoritative for length\/citation targets):\n{{ outputs.paper_preferences | truncate(2000) }}\n",
                    "system": "You plan citation placement for clean BibTeX\/LaTeX manuscripts. You ONLY use cite keys that exist in the provided bibliography — never invent keys."
                },
                "label": "引用计划",
                "label_en": "Citation plan",
                "depends_on": [
                    "outline",
                    "source_pack",
                    "refbib"
                ]
            },
            {
                "id": "writing_plan",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "Synthesize the upstream planning outputs into a single\nauthoritative WRITING_PLAN that every section author must\nobey. Lock terminology, notation, claim mapping, and\nper-section length budget BEFORE any prose is written.\n\nPaper facts:\nTOPIC: {{ outputs.paper_contract | truncate(1200) }}\nMODE: {{ outputs.paper_contract | truncate(400) }}\nLANGUAGE: {{ outputs.paper_contract | truncate(400) }}\nTARGET_PAGES: {{ outputs.paper_contract | truncate(400) }}\nAUDIENCE: {{ outputs.paper_contract | truncate(400) }}\n\nPreferences:\n{{ outputs.paper_preferences | truncate(2000) }}\n\nOutline:\n{{ outputs.outline | truncate(6000) }}\n\nExperiment design:\n{{ outputs.experiment_design | truncate(6000) }}\n\nCitation plan:\n{{ outputs.citation_plan | truncate(6000) }}\n\nBibliography (cite keys MUST come from here):\n{{ outputs.refbib | truncate(4000) }}\n\nFigure placeholders (IDs only):\n{{ outputs.figure_placeholders | truncate(1500) }}\n\nTable placeholders (IDs only):\n{{ outputs.table_placeholders | truncate(1500) }}\n\nLength\/citation budget rules:\n- Treat paper_preferences.LENGTH_STRATEGY and TARGET_LENGTH as\n  authoritative; do not use a fixed default page or word budget when\n  the user requested a different length.\n- This writing plan is the length-control point. Solve length by\n  allocating enough section scope, subclaims, evidence, analysis,\n  and limitations now; do not assume a downstream checker will fix\n  an undersized manuscript later.\n- Convert the requested compiled-page target into an approximate\n  total word budget using the paper language, figure\/table count,\n  and venue style. For normal academic article formatting, set the\n  minimum total target_words to at least TARGET_PAGES × 820 English\n  words (or the equivalent dense prose units for non-English text).\n  Do not reduce below TARGET_PAGES × 760 for figures\/tables; instead\n  add analysis, limitations, related-work synthesis, and method detail.\n- Allocate words across sections according to the requested paper\n  type and contribution shape. A method-heavy paper should give\n  more budget to Method; an empirical paper should give more to\n  Experiments\/Results; a survey should give more to Related Work.\n- The sum of PER_SECTION_BLUEPRINT.*.target_words must meet or\n  exceed the minimum total target_words implied by TARGET_PAGES. If\n  the target is 12 pages, the blueprint should normally allocate at\n  least 9,840 total words across abstract\/introduction\/related_work\/\n  method\/experiments\/discussion\/conclusion.\n- In every PER_SECTION_BLUEPRINT entry, target_words is a\n  lower-bound writing budget. It is not a ceiling. Give each\n  section enough planned subclaims, paragraphs, evidence, analysis,\n  and transitions that a section author can satisfy at least 90% of\n  target_words without padding.\n- Do not return an undersized section from any non-abstract section author.\n- Treat paper_preferences.CITATION_TARGET and CITATION_STRATEGY as\n  authoritative. If they are AUTO, derive a citation budget\n  proportional to target length and available verified references;\n  never invent citations to hit a count.\n- Return explicit per-section target_words and cite_keys budgets\n  that downstream section authors must obey.\n\nReturn EXACTLY this structure (no preamble, no markdown headings):\n\nTITLE:\n<final paper title, ≤16 words>\n\nABSTRACT_DRAFT:\n<120-220 word draft abstract — section authors may polish but\nmay not change the thesis, scope, terminology, or\nPLACEHOLDER_RESULT_TOKEN. Do not invent empirical numbers.>\n\nNARRATIVE_ARC:\n- thesis: <one sentence>\n- story_beats:\n    1. <intro beat>\n    2. <related-work positioning>\n    3. <method core idea>\n    4. <experimental verification>\n    5. <discussion+conclusion takeaway>\n\nKEY_CLAIMS:\n- C1: <one sentence, must be defensible by an experiment>\n- C2: ...\n- ...\n- Cn: ... (5-8 total)\n\nNOTATION_LOCK:\n- symbol: $\\theta$  meaning: model parameters\n- symbol: $\\mathcal{D}$  meaning: dataset\n- (list every symbol that will appear in math)\n\nTERMINOLOGY_LOCK:\n- \"ours\" (proposed method)  forbidden_aliases: [\"our method\", \"the proposed\", \"本文方法\", \"the method\"]\n- \"DPR\" (baseline)  forbidden_aliases: [\"dpr\", \"Dpr\"]\n- ... (every named entity that appears more than once)\n\nPER_SECTION_BLUEPRINT:\n  abstract:\n    target_words: <int>\n    key_claims: [C1, C2, ...]\n    cite_keys: []           # abstract never cites\n    figures: []\n    must_mention: [TITLE, PLACEHOLDER_RESULT_TOKEN]\n  introduction:\n    target_words: <int>\n    key_claims: [C1, C2]\n    cite_keys: [ref_x, ref_y, ...]   # from citation_plan\n    figures: []\n    structure: [motivation, problem, contributions]\n    contributions_count: <int>\n  related_work:\n    target_words: <int>\n    key_claims: []\n    cite_keys: [ref_x, ...]\n    figures: []\n    structure: [survey by axis]\n  method:\n    target_words: <int>\n    key_claims: [C3, C4]\n    cite_keys: [...]\n    figures: [fig1, ...]\n    tables: []\n    structure: [overview → component A → component B → algorithm box]\n    notation_introduced: [θ, f_φ, ...]\n  experiments:\n    target_words: <int>\n    key_claims: [C5, C6]\n    cite_keys: [...]\n    figures: [fig2, ...]\n    tables: [tab1, ...]\n    structure: [setup → main results → ablations]\n    must_include_baselines: [...]\n  discussion:\n    target_words: <int>\n    key_claims: [C7]\n    cite_keys: [...]\n    figures: []\n    structure: [insights → limitations → threats_to_validity]\n  conclusion:\n    target_words: <int>\n    key_claims: [C1-Cn 重申]\n    cite_keys: []\n    figures: []\n    must_call_back_to_abstract: yes\n\nCROSS_SECTION_DEPENDENCIES:\n- method.NOTATION_LOCK symbols MUST be reused verbatim in experiments + discussion\n- intro.contributions_count MUST equal method.structure step count\n- abstract.PLACEHOLDER_RESULT_TOKEN == experiments.PLACEHOLDER_RESULT_TOKEN\n- experiments, discussion, and conclusion MUST use the same\n  qualitative result placeholder until real experiment outputs\n  are supplied; do not state exact numeric improvements.\n\nWRITING_VOICE:\n- tense: <e.g. \"we present \/ we observe\", active>\n- perspective: <e.g. third-person except contributions list>\n- formality: academic; no contractions, no marketing language\n- language: {{ outputs.paper_contract | truncate(400) }}\n\nPLACEHOLDER_RESULT_TOKEN:\n<one stable phrase such as \"the planned evaluation will test\nthe thesis across performance, robustness, and efficiency axes\";\nuse this same phrase in abstract, experiments, discussion, and\nconclusion. Do not invent empirical numbers.>\n",
                    "system": "You build a writing blueprint for a long-form academic manuscript. The blueprint is consumed verbatim by per-section authors; precision matters more than prose."
                },
                "label": "写作计划",
                "label_en": "Writing plan",
                "depends_on": [
                    "paper_preferences",
                    "outline",
                    "citation_plan",
                    "experiment_design",
                    "figure_placeholders",
                    "table_placeholders",
                    "analysis_outline",
                    "refbib"
                ]
            },
            {
                "id": "section_abstract",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the ABSTRACT section. Follow the writing plan\nand produce a single dense paragraph 4-6 sentences covering\nproblem → approach → key result → significance.\n\nsection: abstract\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\noutline:\n{{ outputs.outline | truncate(3000) }}\n\ncitation_plan:\n{{ outputs.citation_plan | truncate(3000) }}\n\ncite_keys_hint:\n{{ outputs.refbib | truncate(2000) }}\n\nOutput rules:\n- Use \\begin{abstract} ... \\end{abstract}.\n- Do not include \\cite{...}.\n- Match TERMINOLOGY_LOCK and NOTATION_LOCK exactly.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.abstract.target_words\n- For the abstract, follow the 4-6 sentence contract first; do not\n  expand it just to satisfy the long-form page target.\n- Only output the LaTeX fragment. No commentary, no fences.\n"
                },
                "label": "摘要段",
                "skill": "paper-section-author",
                "label_en": "Abstract section",
                "depends_on": [
                    "writing_plan"
                ]
            },
            {
                "id": "section_introduction",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the INTRODUCTION section.\n\nsection: introduction\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\nprevious_section_tail (last paragraphs of the abstract):\n{{ outputs.section_abstract | truncate(2000) }}\n\noutline:\n{{ outputs.outline | truncate(3000) }}\n\ncitation_plan (your assigned cite keys are listed under introduction:):\n{{ outputs.citation_plan | truncate(3000) }}\n\ncite_keys_hint (only these keys exist in the bibliography):\n{{ outputs.refbib | truncate(2000) }}\n\nOutput rules:\n- Start with \\section{Introduction}.\n- Structure: motivation → problem → prior-work clusters → gap →\n  our contributions (numbered \\begin{enumerate}) → paper roadmap.\n- Use only cite keys assigned to introduction in citation_plan,\n  and only keys present in cite_keys_hint.\n- Match TERMINOLOGY_LOCK and NOTATION_LOCK exactly.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.introduction.target_words.\n- Length floor: target_words is a lower-bound writing budget. Do\n  not return until the section reaches at least 90% of target_words;\n  expand with plan-aligned motivation, prior-work contrast,\n  contribution detail, and roadmap prose if short. Do not return an\n  undersized section.\n- Output ONLY the LaTeX fragment for this section. No fences.\n"
                },
                "label": "引言段",
                "skill": "paper-section-author",
                "label_en": "Introduction section",
                "depends_on": [
                    "writing_plan",
                    "section_abstract"
                ]
            },
            {
                "id": "section_related_work",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the RELATED WORK section.\n\nsection: related_work\n\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\nprevious_section_tail (last paragraphs of the introduction):\n{{ outputs.section_introduction | truncate(2000) }}\n\noutline:\n{{ outputs.outline | truncate(3000) }}\n\ncitation_plan (your assigned cite keys are listed under related_work:):\n{{ outputs.citation_plan | truncate(3000) }}\n\ncite_keys_hint (only these keys exist in the bibliography):\n{{ outputs.refbib | truncate(2500) }}\n\nOutput rules:\n- Start with \\section{Related Work}.\n- Survey by 2-4 thematic axes (e.g. efficiency \/ fidelity \/\n  agentic \/ dataset construction). Use \\subsection for each.\n- Cite from your assigned keys; do not introduce new claims.\n- Do NOT include figures\/tables here.\n- Match TERMINOLOGY_LOCK exactly.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.related_work.target_words.\n- Length floor: target_words is a lower-bound writing budget. Do\n  not return until the section reaches at least 90% of target_words;\n  expand with plan-aligned thematic comparisons, citation synthesis,\n  and explicit gap analysis if short. Do not return an undersized\n  section.\n- Output ONLY the LaTeX fragment. No fences, no preamble.\n"
                },
                "label": "相关工作",
                "skill": "paper-section-author",
                "label_en": "Related work",
                "depends_on": [
                    "writing_plan",
                    "section_introduction"
                ]
            },
            {
                "id": "section_method",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the METHOD section.\n\nsection: method\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\nprevious_section_tail (last paragraphs of related work):\n{{ outputs.section_related_work | truncate(2000) }}\n\noutline:\n{{ outputs.outline | truncate(3000) }}\n\ncitation_plan:\n{{ outputs.citation_plan | truncate(3000) }}\n\ncite_keys_hint:\n{{ outputs.refbib | truncate(2500) }}\n\nfigure_placeholders (you may reference these via \\ref{fig:<id>} when relevant):\n{{ outputs.figure_placeholders | truncate(2000) }}\n\nOutput rules:\n- Start with \\section{Method}.\n- Use \\subsection{Setup}, \\subsection{Algorithm} (or {Approach}),\n  \\subsection{Instrumentation}, and \\subsection{Baselines}.\n- Introduce notation per writing_plan.NOTATION_LOCK\n  (every symbol used later in experiments\/discussion MUST\n  be defined here).\n- You may inline ONE figure environment from figure_placeholders\n  that supports method exposition; reference it via \\ref{fig:<id>}.\n- Match TERMINOLOGY_LOCK \/ NOTATION_LOCK exactly.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.method.target_words.\n- Length floor: target_words is a lower-bound writing budget. Do\n  not return until the section reaches at least 90% of target_words;\n  expand with plan-aligned assumptions, definitions, algorithmic\n  detail, instrumentation, and reproducibility notes if short. Do\n  not return an undersized section.\n- Output ONLY the LaTeX fragment. No fences.\n"
                },
                "label": "方法段",
                "skill": "paper-section-author",
                "label_en": "Methods section",
                "depends_on": [
                    "writing_plan",
                    "section_related_work",
                    "figure_placeholders"
                ]
            },
            {
                "id": "section_experiments",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the EXPERIMENTS \/ RESULTS section. Use the\npaper-section-author \"results\" contract.\n\nsection: results\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\nprevious_section_tail (last paragraphs of method):\n{{ outputs.section_method | truncate(2500) }}\n\noutline:\n{{ outputs.outline | truncate(3000) }}\n\ncitation_plan:\n{{ outputs.citation_plan | truncate(3000) }}\n\ncite_keys_hint:\n{{ outputs.refbib | truncate(2500) }}\n\nfigure_placeholders (inline ALL remaining figures here):\n{{ outputs.figure_placeholders | truncate(4000) }}\n\ntable_placeholders (inline ALL tables here):\n{{ outputs.table_placeholders | truncate(4000) }}\n\nOutput rules:\n- Start with \\section{Experiments}.\n- Inline EVERY figure and table from figure_placeholders \/\n  table_placeholders that has not already been inlined in method.\n- Reference via \\ref{fig:<id>} and \\ref{tab:<id>}.\n- Structure: \\subsection{Setup} → \\subsection{Main Results} →\n  \\subsection{Ablations} → \\subsection{Sensitivity}.\n- Use writing_plan.PLACEHOLDER_RESULT_TOKEN for the headline\n  evidence claim. Do not state exact numeric improvements,\n  percentages, scores, latency reductions, or win rates unless\n  they are explicitly present in user-provided experiment data.\n- Use ONLY notation\/terminology locked in writing_plan.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.experiments.target_words.\n- Length floor: target_words is a lower-bound writing budget. Do\n  not return until the section reaches at least 90% of target_words;\n  expand with plan-aligned setup, metric rationale, baseline\n  comparison, ablation interpretation, sensitivity analysis, and\n  failure-case discussion if short. Do not return an undersized\n  section.\n- Output ONLY the LaTeX fragment. No fences.\n"
                },
                "label": "实验段",
                "skill": "paper-section-author",
                "label_en": "Experiments section",
                "depends_on": [
                    "writing_plan",
                    "section_method",
                    "figure_placeholders",
                    "table_placeholders"
                ]
            },
            {
                "id": "section_discussion",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the DISCUSSION section.\n\nsection: discussion\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\nprevious_section_tail (last paragraphs of experiments):\n{{ outputs.section_experiments | truncate(2500) }}\n\noutline:\n{{ outputs.outline | truncate(3000) }}\n\ncitation_plan:\n{{ outputs.citation_plan | truncate(3000) }}\n\ncite_keys_hint:\n{{ outputs.refbib | truncate(2500) }}\n\nanalysis_outline (use this as the structural blueprint):\n{{ outputs.analysis_outline | truncate(4000) }}\n\nOutput rules:\n- Start with \\section{Discussion}.\n- Inline the analysis_outline subsections verbatim where they\n  fit, but expand each with 1-2 paragraphs of substantive\n  commentary referencing concrete experiment results.\n- End the section with explicit \\subsection{Limitations} and\n  \\subsection{Threats to Validity}.\n- Match TERMINOLOGY_LOCK \/ NOTATION_LOCK exactly.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.discussion.target_words.\n- Length floor: target_words is a lower-bound writing budget. Do\n  not return until the section reaches at least 90% of target_words;\n  expand with plan-aligned interpretation, boundary conditions,\n  limitations, threats to validity, and implications if short. Do\n  not return an undersized section.\n- Output ONLY the LaTeX fragment.\n"
                },
                "label": "讨论段",
                "skill": "paper-section-author",
                "label_en": "Discussion section",
                "depends_on": [
                    "writing_plan",
                    "section_experiments",
                    "analysis_outline"
                ]
            },
            {
                "id": "section_conclusion",
                "kind": "agent",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "You are writing the CONCLUSION section. Must close the loop on the abstract.\n\nsection: conclusion\n\nwriting_plan:\n{{ outputs.writing_plan | truncate(8000) }}\n\nabstract (the conclusion must echo its claims):\n{{ outputs.section_abstract | truncate(1500) }}\n\nprevious_section_tail (discussion ending):\n{{ outputs.section_discussion | truncate(2000) }}\n\nOutput rules:\n- Start with \\section{Conclusion}.\n- Cover: 1) restated thesis + headline result, 2) key contributions\n  reiterated, 3) scope and limitations, 4) future-work pointer. Use\n  as many concise paragraphs as the writing_plan target_words\n  requires; do not cap the conclusion at 2-3 paragraphs when the\n  requested page target is long.\n- No new claims, no new figures, no \\cite{}.\n- Match TERMINOLOGY_LOCK exactly.\n- target_words from writing_plan.PER_SECTION_BLUEPRINT.conclusion.target_words.\n- Length floor: target_words is a lower-bound writing budget. Do\n  not return until the section reaches at least 90% of target_words;\n  expand with plan-aligned synthesis and implications if short. Do\n  not return an undersized section.\n- Output ONLY the LaTeX fragment.\n"
                },
                "label": "结论段",
                "skill": "paper-section-author",
                "label_en": "Conclusion section",
                "depends_on": [
                    "writing_plan",
                    "section_discussion",
                    "section_abstract"
                ]
            },
            {
                "id": "persist_sections",
                "kind": "tool_call",
                "tool": "exec_command",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "label": "保存章节",
                "label_en": "Save sections",
                "tool_args": {
                    "env": {
                        "SEC_INTRO": "{{ outputs.section_introduction }}",
                        "SEC_METHOD": "{{ outputs.section_method }}",
                        "SEC_RELATED": "{{ outputs.section_related_work }}",
                        "SEC_ABSTRACT": "{{ outputs.section_abstract }}",
                        "SEC_CONCLUSION": "{{ outputs.section_conclusion }}",
                        "SEC_DISCUSSION": "{{ outputs.section_discussion }}",
                        "SEC_EXPERIMENTS": "{{ outputs.section_experiments }}"
                    },
                    "command": "python3 - <<'PY'\nimport os, re\nfrom pathlib import Path\n\ndef clean(text):\n    text = re.sub(r'^```(?:latex|tex)?\\s*\\n', '', text or '', flags=re.MULTILINE)\n    text = re.sub(r'\\n```\\s*$', '', text)\n    return text.strip()\n\nsections = {\n    'abstract':     os.environ.get('SEC_ABSTRACT', ''),\n    'introduction': os.environ.get('SEC_INTRO', ''),\n    'related_work': os.environ.get('SEC_RELATED', ''),\n    'method':       os.environ.get('SEC_METHOD', ''),\n    'experiments':  os.environ.get('SEC_EXPERIMENTS', ''),\n    'discussion':   os.environ.get('SEC_DISCUSSION', ''),\n    'conclusion':   os.environ.get('SEC_CONCLUSION', ''),\n}\nout_dir = Path('paper') \/ 'sections'\nout_dir.mkdir(parents=True, exist_ok=True)\n\nprint('SECTION_ARTIFACTS:')\ntotal = 0\nfor name, text in sections.items():\n    body = clean(text)\n    path = out_dir \/ f'{name}.tex'\n    path.write_text(body, encoding='utf-8')\n    chars = len(body)\n    total += chars\n    first_line = next((line.strip() for line in body.splitlines() if line.strip()), '')\n    print(f'- {name}: path={path.as_posix()} chars={chars} first_line={first_line[:120]!r}')\nprint(f'TOTAL_SECTION_CHARS: {total}')\nprint('CONTEXT_POLICY: downstream steps must read section files from disk and pass only paths\/summaries to LLM prompts')\nPY\n",
                    "timeout": 30,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "section_abstract",
                    "section_introduction",
                    "section_related_work",
                    "section_method",
                    "section_experiments",
                    "section_discussion",
                    "section_conclusion"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "assemble_manuscript_tex",
                "kind": "tool_call",
                "tool": "exec_command",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "label": "组装 TEX",
                "label_en": "Assemble TEX",
                "tool_args": {
                    "env": {
                        "TOPIC": "{{ outputs.paper_contract | truncate(400) }}",
                        "BIB_TEXT": "{{ outputs.refbib }}",
                        "WRITING_PLAN": "{{ outputs.writing_plan }}"
                    },
                    "command": "python3 - <<'PY'\nimport os, re, sys\nfrom pathlib import Path\n\nsection_dir = Path('paper') \/ 'sections'\nsections = {\n    'abstract':     section_dir \/ 'abstract.tex',\n    'introduction': section_dir \/ 'introduction.tex',\n    'related_work': section_dir \/ 'related_work.tex',\n    'method':       section_dir \/ 'method.tex',\n    'experiments':  section_dir \/ 'experiments.tex',\n    'discussion':   section_dir \/ 'discussion.tex',\n    'conclusion':   section_dir \/ 'conclusion.tex',\n}\nsection_text = {\n    name: path.read_text(encoding='utf-8') if path.is_file() else ''\n    for name, path in sections.items()\n}\nbib = os.environ.get('BIB_TEXT', '').strip()\n# Extract TITLE from the writing_plan envelope. Falls back to the\n# raw topic when the LLM omits a TITLE line so the PDF gets a\n# meaningful title regardless.\nwriting_plan = os.environ.get('WRITING_PLAN', '')\ntopic_fallback = os.environ.get('TOPIC', 'Untitled Manuscript')\ntm = re.search(r'^\\s*TITLE\\s*:\\s*(.+?)\\s*$', writing_plan, re.MULTILINE)\nraw_title = (tm.group(1).strip() if tm else topic_fallback) or topic_fallback\n# LaTeX-escape the title so user-provided text can't break the preamble.\ndef latex_escape(s):\n    s = s.replace('\\\\', r'\\textbackslash{}')\n    for ch in '&%$#_{}':\n        s = s.replace(ch, '\\\\' + ch)\n    s = s.replace('~', r'\\textasciitilde{}')\n    s = s.replace('^', r'\\textasciicircum{}')\n    return s\n\ndef scrub_placeholder_table_cells(tex):\n    \"\"\"Scrub numeric-looking data cells from placeholder tables.\"\"\"\n    numeric = re.compile(\n        r'^\\s*(?:\\\\textbf\\{)?[-+]?\\d[\\d,]*(?:\\.\\d+)?\\s*(?:%|ms|s|x|MB|GB|points?)?(?:\\})?\\s*$',\n        re.I,\n    )\n    out = []\n    in_tabular = False\n    after_midrule = False\n    for line in tex.splitlines():\n        if r'\\begin{tabular}' in line:\n            in_tabular = True\n            after_midrule = False\n            out.append(line)\n            continue\n        if in_tabular and r'\\end{tabular}' in line:\n            in_tabular = False\n            after_midrule = False\n            out.append(line)\n            continue\n        if in_tabular and r'\\midrule' in line:\n            after_midrule = True\n            out.append(line)\n            continue\n        if in_tabular and after_midrule and '&' in line and r'\\bottomrule' not in line:\n            suffix = r' \\\\' if line.rstrip().endswith(r'\\\\') else ''\n            row = line.rstrip()\n            if suffix:\n                row = row[:-2].rstrip()\n            cells = [cell.strip() for cell in row.split('&')]\n            if len(cells) > 1:\n                cells = [cells[0], *('---' if numeric.match(cell) else cell for cell in cells[1:])]\n                indent = re.match(r'^\\s*', line).group(0)\n                line = indent + ' & '.join(cells) + suffix\n        out.append(line)\n    return '\\n'.join(out)\ntitle_tex = latex_escape(raw_title)\n# Build preamble — load xeCJK if title or any section has CJK\nany_cjk = (re.search(r'[一-鿿]', raw_title) is not None) or any(\n    re.search(r'[一-鿿]', v) for v in section_text.values()\n)\npreamble = [\n    r\"\\documentclass{article}\",\n    r\"\\usepackage{xeCJK}\" if any_cjk else r\"% no CJK\",\n    r\"\\usepackage{graphicx}\",\n    r\"\\usepackage{booktabs}\",\n    r\"\\usepackage{amsmath,amssymb}\",\n    r\"\\usepackage{hyperref}\",\n    r\"\\usepackage{geometry}\",\n    r\"\\geometry{margin=2.5cm}\",\n    r\"\\title{\" + title_tex + r\"}\",\n    r\"\\author{OpenSquilla meta-paper-write}\",\n    r\"\\date{\\today}\",\n    r\"\\begin{document}\",\n    r\"\\maketitle\",\n]\nbody_parts = [\n    section_text['abstract'],     # \\begin{abstract}...\\end{abstract}\n    section_text['introduction'], # \\section{Introduction}...\n    section_text['related_work'],\n    section_text['method'],\n    section_text['experiments'],\n    section_text['discussion'],\n    section_text['conclusion'],\n]\ntail = [\n    r\"\\bibliographystyle{plain}\",\n    r\"\\bibliography{references}\",\n    r\"\\end{document}\",\n]\ntex = '\\n'.join(preamble) + '\\n\\n' + '\\n\\n'.join(p for p in body_parts if p) + '\\n\\n' + '\\n'.join(tail)\ntex = scrub_placeholder_table_cells(tex)\npaper_dir = Path('paper')\npaper_dir.mkdir(exist_ok=True)\ntex_path = paper_dir \/ 'paper.tex'\nbib_path = paper_dir \/ 'references.bib'\ntex_path.write_text(tex, encoding='utf-8')\nbib_path.write_text(bib if bib else '% no verified references', encoding='utf-8')\nprint(f'MANUSCRIPT_PATH: {tex_path.resolve()}')\nprint(f'REFERENCES_PATH: {bib_path.resolve()}')\nprint(f'MANUSCRIPT_CHARS: {len(tex)}')\nprint(f'REFERENCES_CHARS: {len(bib)}')\nprint('COMPILE_NOTES:')\nprint('- assembled section-by-section via paper-section-author')\nprint(f'- sections present: {\", \".join(k for k, v in section_text.items() if v)}')\nprint(f'- total section chars: {sum(len(v) for v in section_text.values())}')\nprint('- context policy: full manuscript persisted on disk; downstream prompts should use path\/summary only')\nPY\n",
                    "timeout": 30,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "writing_plan",
                    "persist_sections",
                    "refbib"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "consistency_pass",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract",
                "with": {
                    "task": "Review the assembled manuscript manifest against the writing plan.\nDo NOT request or reproduce the full manuscript text in this step.\nThe full manuscript is persisted on disk; keep this output compact\nso long paper runs do not trigger repeated context compaction.\n\nDrift to check:\n1. Terminology: any synonym variant of a TERMINOLOGY_LOCK term\n   should be flagged for later repair.\n2. Notation: any math symbol that disagrees with NOTATION_LOCK\n   should be flagged.\n3. Numbers: if abstract \/ experiments \/ discussion mention the\n   same headline metric with different values, flag the drift.\n4. Cite keys: ensure every \\cite{...} key exists in the\n   REFERENCES_BIB block; citation_map performs the exact parse.\n5. Section ordering: keep abstract → intro → related → method →\n   experiments → discussion → conclusion.\n\nWriting plan (authoritative):\n{{ outputs.writing_plan | truncate(8000) }}\n\nAssembled manuscript manifest:\n{{ outputs.assemble_manuscript_tex | truncate(2000) }}\n\nOutput EXACTLY:\nMANUSCRIPT_PATH: <copy MANUSCRIPT_PATH from assembled manifest>\nREFERENCES_PATH: <copy REFERENCES_PATH from assembled manifest>\nCOMPILE_NOTES:\n- consistency_findings: <one line per possible drift, OR \"none\">\nCONTEXT_POLICY: artifact-only; full manuscript omitted from prompt\/output\n",
                    "system": "You are the consistency auditor for an academic manuscript. You inspect compact manifests and return actionable checks without rewriting the full manuscript."
                },
                "label": "一致性检查",
                "label_en": "Consistency check",
                "depends_on": [
                    "writing_plan",
                    "assemble_manuscript_tex"
                ]
            },
            {
                "id": "final_manuscript_package",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract",
                "with": {
                    "task": "Draft a full manuscript package. The default output must be clean\nLaTeX-ready paper text, not planning notes. Do not include markdown\nfences, chat commentary, progress notes, or tool logs.\n\nPaper mode:\nTOPIC: {{ outputs.paper_contract | truncate(1200) }}, MODE: {{ outputs.paper_contract | truncate(400) }}, PAGES: {{ outputs.paper_contract | truncate(400) }}\n\nMode behavior:\n- FULL_MANUSCRIPT: produce enough substance for\n  TARGET_LENGTH from paper_preferences as compiled pages, using\n  the user-requested or derived CITATION_TARGET instead of a fixed\n  reference count. Distribute verified citation keys across\n  abstract, introduction, related work, method, results, discussion,\n  limitations, and conclusion.\n- COMPACT_SKELETON: produce a compact LaTeX-ready manuscript\n  skeleton with section goals, planned citations, and expansion\n  notes; do not pretend it is a finished paper of the requested\n  length. For this\n  mode, the final package MUST include an explicit manuscript plan,\n  a target-length expansion plan, limitations\/threats-to-validity,\n  and reference placeholders sized to the requested\/derived citation\n  strategy when verified BibTeX entries are unavailable. Keep the compact package short enough that all\n  required sections are visible before any evaluator truncation:\n  put the plan and expansion plan before the LaTeX skeleton, and\n  keep MANUSCRIPT_TEX under 2,500 words.\n- REPAIR_EXISTING: return a repaired clean LaTeX package focused on\n  citation integrity, structure, and removal of process text.\n- COMPILE_ONLY: return a compile handoff package and blockers only;\n  do not invent missing manuscript body.\n\nCITATION CONTRACT (load-bearing):\n- DO NOT invent cite keys. Use ONLY keys that appear verbatim in\n  REFERENCES_BIB below.\n- DO NOT cite a key that REFERENCES_BIB does not contain.\n- Every claim that needs evidence MUST cite at least one key from\n  REFERENCES_BIB.\n- Distribute citations according to paper_preferences.CITATION_STRATEGY;\n  avoid repeatedly citing one key when enough verified sources exist.\n- If REFERENCES_BIB is empty or lacks enough verified entries, do\n  not emit \\cite{...}. Use visible placeholders such as\n  [REF-01 needed: agent benchmark survey] in the LaTeX text and\n  list them under REFERENCE_PLACEHOLDERS instead. Placeholder\n  references are safer than fabricated BibTeX.\n\nFIGURE\/TABLE CONTRACT:\n- Inline the figure_placeholders block verbatim into Results.\n- Inline the table_placeholders block verbatim into Method or\n  Results (split by purpose).\n- Inline the analysis_outline block verbatim into Discussion.\n- Reference figures\/tables via \\\\ref{fig:<id>} and \\\\ref{tab:<id>}\n  where they appear in the body; never reference an id not present\n  in the placeholders.\n\nPaper preferences:\n{{ outputs.paper_preferences | truncate(2000) }}\n\nOutline:\n{{ outputs.outline | truncate(8000) }}\n\nCitation plan:\n{{ outputs.citation_plan | truncate(8000) }}\n\nFigure placeholders (inline this verbatim somewhere in Results):\n{{ outputs.figure_placeholders | truncate(4000) }}\n\nTable placeholders (inline this verbatim in Method\/Results):\n{{ outputs.table_placeholders | truncate(4000) }}\n\nAnalysis outline (inline this verbatim in Discussion):\n{{ outputs.analysis_outline | truncate(4000) }}\n\nBibliography (cite keys MUST come from here):\n{{ outputs.refbib | truncate(8000) }}\n\nCRITICAL OUTPUT CONTRACT (load-bearing — the downstream\ncompile_pdf step parses these markers literally):\n\n- The MANUSCRIPT_TEX section is MANDATORY and MUST come first.\n  It MUST start with the literal token `MANUSCRIPT_TEX:` on its\n  own line, immediately followed by `\\documentclass{article}`\n  and end with `\\end{document}`. Do NOT wrap in ```latex\n  fences. Do NOT prefix with markdown headings.\n- If you find yourself running out of tokens, shorten section\n  bodies — DO NOT omit MANUSCRIPT_TEX. A short complete\n  \\documentclass…\\end{document} block is FAR more useful than\n  a long MANUSCRIPT_PLAN with no LaTeX.\n- REFERENCES_BIB is the second mandatory section. Use\n  `REFERENCES_BIB:` on its own line followed by BibTeX entries.\n  If the bibliography is empty, output `REFERENCES_BIB:`\n  followed by a single line `% no verified references` (the\n  \\cite{} keys in MANUSCRIPT_TEX should then be visible\n  placeholders, not BibTeX-keyed cites).\n\nReturn EXACTLY in this order (no preamble, no markdown headings):\n\nMANUSCRIPT_TEX:\n\\documentclass{article}\n\\usepackage{xeCJK}\n\\usepackage{graphicx}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\usepackage{hyperref}\n\\title{...}\n\\author{...}\n\\date{\\today}\n\\begin{document}\n\\maketitle\n\\begin{abstract}...\\end{abstract}\n\\section{Introduction}...\n\\section{Related Work}...\n\\section{Method}...\n\\section{Experiments}...\n(inline the figure_placeholders, table_placeholders, and\nanalysis_outline blocks verbatim where appropriate)\n\\section{Discussion}...\n\\section{Limitations}...\n\\section{Threats to Validity}...\n\\section{Conclusion}...\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\end{document}\n\nREFERENCES_BIB:\n<BibTeX entries copied verbatim from the provided bibliography —\nonly the entries actually cited in MANUSCRIPT_TEX. If empty,\noutput a single `% no verified references` line.>\n\nMANUSCRIPT_PLAN:\n- (optional) section-by-section plan with target pages and\n  contribution. Skip this section if MANUSCRIPT_TEX is already\n  tight on tokens.\n\nTARGET_LENGTH_EXPANSION_PLAN:\n- For COMPACT_SKELETON, list the concrete section expansions,\n  extra experiments, figures, tables, and citation work needed\n  to grow this package into the user-requested target length.\n\nREFERENCE_PLACEHOLDERS:\n- (optional) placeholder reference notes if REFERENCES_BIB is\n  empty or sparse.\n\nCOMPILE_NOTES:\n- <short note about figure\/reference assumptions>\n",
                    "system": "You write clean LaTeX manuscripts. Output only the requested manuscript package. NEVER invent cite keys — every \\cite{...} you emit MUST exist verbatim in REFERENCES_BIB below."
                },
                "label": "终稿打包",
                "label_en": "Final package",
                "depends_on": [
                    "paper_contract",
                    "outline",
                    "citation_plan",
                    "refbib",
                    "figure_placeholders",
                    "table_placeholders",
                    "analysis_outline"
                ]
            },
            {
                "id": "citation_map",
                "kind": "tool_call",
                "tool": "exec_command",
                "when": "'PAPER_MODE: COMPILE_ONLY' not in outputs.paper_contract",
                "label": "引用映射",
                "label_en": "Citation mapping",
                "tool_args": {
                    "env": {
                        "REFBIB": "{{ outputs.refbib }}",
                        "MANIFEST": "{{ outputs.get('consistency_pass') or outputs.get('assemble_manuscript_tex') or outputs.get('final_manuscript_package', '') }}"
                    },
                    "command": "python3 - <<'PY'\nimport os, re\nfrom pathlib import Path\n\npkg = os.environ.get('MANIFEST', '')\nm = re.search(r'MANUSCRIPT_PATH:\\s*(.+)', pkg)\nb = re.search(r'REFERENCES_PATH:\\s*(.+)', pkg)\ntex_path = Path(m.group(1).strip()) if m else Path('paper\/paper.tex')\nbib_path = Path(b.group(1).strip()) if b else Path('paper\/references.bib')\n\ntex = tex_path.read_text(encoding='utf-8', errors='ignore') if tex_path.is_file() else ''\nbib = bib_path.read_text(encoding='utf-8', errors='ignore') if bib_path.is_file() else os.environ.get('REFBIB', '')\n\ncite_counts = {}\nfor group in re.findall(r'\\\\cite\\{([^}]+)\\}', tex):\n    for key in [k.strip() for k in group.split(',') if k.strip()]:\n        cite_counts[key] = cite_counts.get(key, 0) + 1\n\nentries = {}\nfor match in re.finditer(r'@\\w+\\s*\\{\\s*([^,\\s]+)\\s*,(.*?)(?=\\n@\\w+\\s*\\{|\\Z)', bib, re.DOTALL):\n    key = match.group(1).strip()\n    body = match.group(2)\n    title = re.search(r'title\\s*=\\s*[\\{\\\"]([^}\\\"]+)', body, re.I)\n    url = re.search(r'(?:url|howpublished)\\s*=\\s*[\\{\\\"]([^}\\\"]+)', body, re.I)\n    doi = re.search(r'doi\\s*=\\s*[\\{\\\"]([^}\\\"]+)', body, re.I)\n    eprint = re.search(r'eprint\\s*=\\s*[\\{\\\"]([^}\\\"]+)', body, re.I)\n    locator = (url.group(1) if url else '') or (f'doi:{doi.group(1)}' if doi else '') or (f'arXiv:{eprint.group(1)}' if eprint else '')\n    entries[key] = {\n        'title': title.group(1).strip() if title else '',\n        'locator': locator,\n    }\n\nstrong_domains = ('arxiv.org', 'aclanthology.org', 'dl.acm.org', 'openreview.net', 'ieee.org', 'nature.com', 'science.org', 'biorxiv.org', 'pnas.org')\nweak_markers = ('medium.com', 'wikipedia.org', 'github.com', 'stackoverflow.com', 'twitter.com', 'x.com')\ndef quality(locator, invalid=False):\n    low = locator.lower()\n    if invalid:\n        return 'INVALID'\n    if any(d in low for d in strong_domains) or 'doi:' in low or 'arxiv:' in low:\n        return 'STRONG'\n    if any(w in low for w in weak_markers):\n        return 'WEAK'\n    if locator:\n        return 'OK'\n    return 'WEAK'\n\nrows = []\ninvalid = weak = strong = ok = unused = 0\nall_keys = sorted(set(cite_counts) | set(entries))\nprint('CITATION_MAP:')\nprint()\nprint('| Cite Key | Cited Times | Title | URL \/ DOI \/ arXiv | Source Quality |')\nprint('|---|---:|---|---|---|')\nfor key in all_keys:\n    count = cite_counts.get(key, 0)\n    entry = entries.get(key)\n    invalid_row = entry is None\n    q = quality(entry['locator'] if entry else '', invalid=invalid_row)\n    if invalid_row:\n        invalid += 1\n    elif count == 0:\n        unused += 1\n        q = 'UNUSED'\n    elif q == 'STRONG':\n        strong += 1\n    elif q == 'OK':\n        ok += 1\n    elif q == 'WEAK':\n        weak += 1\n    title = entry['title'] if entry else '(MISSING IN BIB)'\n    locator = entry['locator'] if entry else '-'\n    print(f'| {key} | {count} | {title} | {locator} | {q} |')\nprint()\nprint(f'SUMMARY: total_cite_keys={len(cite_counts)}, strong={strong}, ok={ok}, weak={weak}, invalid={invalid}, unused={unused}')\nprint(f'ARTIFACTS: manuscript={tex_path} references={bib_path}')\nPY\n",
                    "timeout": 30,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "final_manuscript_package",
                    "consistency_pass",
                    "assemble_manuscript_tex",
                    "refbib"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "paper_length_gate",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract",
                "with": {
                    "task": "Check whether the manuscript package satisfies the requested paper\nlength, section coverage, and compact\/skeleton mode constraints.\n\nPaper preferences:\n{{ outputs.paper_preferences | truncate(4000) }}\n\nManuscript package:\n{{ outputs.get('consistency_pass') or outputs.get('assemble_manuscript_tex') or outputs.get('final_manuscript_package', '') | truncate(8000) }}\n\nReply with:\nLENGTH_GATE: <pass|warn|block>\nESTIMATED_WORDS: <int or unknown>\nBLOCKERS:\n  - <blocker or none>\nWARNINGS:\n  - <warning or none>\n",
                    "system": "You verify manuscript length requirements before final packaging."
                },
                "label": "篇幅门禁",
                "label_en": "Length gate",
                "depends_on": [
                    "final_manuscript_package",
                    "consistency_pass",
                    "assemble_manuscript_tex"
                ]
            },
            {
                "id": "citation_integrity_gate",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract",
                "with": {
                    "task": "Validate citation integrity before LaTeX compilation.\n\nRequirements (LOAD-BEARING — block compilation if any fails):\n- REFERENCES_BIB and body citations satisfy the user-requested or\n  derived CITATION_TARGET from paper_preferences when sources allow it\n- distinct citation keys used\/planned in the body match\n  paper_preferences.CITATION_STRATEGY; do not enforce a fixed count\n- NO citation keys absent from references.bib (citation_map column\n  \"INVALID\" must be 0)\n- every cited entry MUST have a verifiable URL or DOI or arXiv\n  eprint field in REFERENCES_BIB; entries with only howpublished\n  text are degraded but acceptable; entries with no URL\/DOI\/eprint\n  at all are blockers\n- no Source Quality == WEAK in citation_map for primary claims\n  (introduction headline \/ method core \/ results headline);\n  warn but do not block for related-work \/ motivation context\n- every major claim has nearby citation support or an explicit caveat\n\nCitation plan:\n{{ outputs.citation_plan | truncate(8000) }}\n\nBibliography:\n{{ outputs.refbib | truncate(8000) }}\n\nCitation audit table (read this — do NOT re-derive):\n{{ outputs.citation_map | truncate(4000) }}\n\nReply with:\nINTEGRITY: <pass|warn|block>\nINVALID_COUNT: <int>\nWEAK_PRIMARY_COUNT: <int>\nUNUSED_COUNT: <int>\nBLOCKERS:\n  - <blocker or none>\nWARNINGS:\n  - <warning or none>\n",
                    "system": "You verify LaTeX\/BibTeX citation integrity."
                },
                "label": "引用门禁",
                "label_en": "Citation gate",
                "depends_on": [
                    "final_manuscript_package",
                    "consistency_pass",
                    "assemble_manuscript_tex",
                    "citation_plan",
                    "refbib",
                    "citation_map",
                    "paper_length_gate"
                ]
            },
            {
                "id": "latex_sanitizer",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract or 'PAPER_MODE: COMPILE_ONLY' in outputs.paper_contract",
                "with": {
                    "task": "Sanitize the final LaTeX package contract before compilation. Confirm\nthat process commentary, markdown fences, chat preambles, debug logs,\nand non-paper text are absent from MANUSCRIPT_TEX and REFERENCES_BIB.\nPreserve valid LaTeX, CJK text, citations, figure references,\nplaceholder figure\/table blocks (\\fbox + tabular), and section content.\nReply with a concise readiness note and any blocking issue only.\n\nCitation gate:\n{{ outputs.citation_integrity_gate | truncate(2000) }}\n",
                    "system": "You sanitize LaTeX deliverables and reject process text."
                },
                "label": "LaTeX 清理",
                "label_en": "LaTeX cleanup",
                "depends_on": [
                    "citation_integrity_gate"
                ]
            },
            {
                "id": "compile_latex",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: COMPILE_ONLY' in outputs.paper_contract",
                "with": {
                    "task": "Produce a concise compile handoff note. COMPILE_ONLY is for\nassessing an existing LaTeX manuscript. The full manuscript and\ncompact skeleton paths compile a PDF via compile_pdf after quality\ngates pass.\n\nSanitizer result:\n{{ outputs.latex_sanitizer | truncate(2000) }}\n\nReply exactly:\nCOMPILE_READY: <yes|blocked>\nNEXT_STEP: provide or select an existing manuscript package to compile\nBLOCKERS:\n  - <blocker or none>\n",
                    "system": "You prepare compile-only handoff notes without invoking LaTeX in this step."
                },
                "label": "编译 LaTeX",
                "label_en": "Compile LaTeX",
                "depends_on": [
                    "latex_sanitizer"
                ]
            },
            {
                "id": "compile_pdf",
                "kind": "tool_call",
                "tool": "exec_command",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract",
                "label": "编译 PDF",
                "label_en": "Compile PDF",
                "tool_args": {
                    "env": {
                        "MANUSCRIPT_PKG": "{{ outputs.get('consistency_pass') or outputs.get('assemble_manuscript_tex') or outputs.get('final_manuscript_package', '') }}"
                    },
                    "command": "python3 - <<'PY'\nimport os, re, subprocess, sys\nfrom pathlib import Path\n\npkg = os.environ.get('MANUSCRIPT_PKG', '')\n\n# 1. Try MANUSCRIPT_TEX: \/ REFERENCES_BIB: contract markers first.\nm = re.search(r'MANUSCRIPT_TEX:\\s*(.+?)(?:REFERENCES_BIB:|COMPILE_NOTES:|\\Z)', pkg, re.DOTALL)\ntex_body = m.group(1).strip() if m else ''\nmb = re.search(r'REFERENCES_BIB:\\s*(.+?)(?:COMPILE_NOTES:|\\Z)', pkg, re.DOTALL)\nbib = mb.group(1).strip() if mb else ''\n\n# 2. Fallback A: maybe LLM wrapped LaTeX in ```latex fences without the marker.\nif not tex_body:\n    fenced = re.search(r'```(?:latex|tex)?\\s*(\\\\documentclass[\\s\\S]+?\\\\end\\{document\\})', pkg)\n    if fenced:\n        tex_body = fenced.group(1).strip()\n\n# 3. Fallback B: maybe there's a raw \\documentclass…\\end{document} block.\nif not tex_body:\n    raw = re.search(r'(\\\\documentclass[\\s\\S]+?\\\\end\\{document\\})', pkg)\n    if raw:\n        tex_body = raw.group(1).strip()\n\n# 4. Fallback C: artifact-only FULL_MANUSCRIPT path. Read the\n# persisted manuscript and bibliography from disk instead of\n# requiring the full document to be present in the meta context.\nmanifest_tex_path = None\nmanifest_bib_path = None\nif not tex_body:\n    pm = re.search(r'MANUSCRIPT_PATH:\\s*(.+)', pkg)\n    bm = re.search(r'REFERENCES_PATH:\\s*(.+)', pkg)\n    if pm:\n        manifest_tex_path = Path(pm.group(1).strip())\n        if manifest_tex_path.is_file():\n            tex_body = manifest_tex_path.read_text(encoding='utf-8')\n    if bm:\n        manifest_bib_path = Path(bm.group(1).strip())\n        if manifest_bib_path.is_file():\n            bib = manifest_bib_path.read_text(encoding='utf-8')\n\n# 5. Strip any leftover markdown fences from extracted bodies.\ntex_body = re.sub(r'^```(?:latex|tex)?\\s*\\n', '', tex_body)\ntex_body = re.sub(r'\\n```\\s*$', '', tex_body)\n\ndef scrub_placeholder_table_cells(tex):\n    \"\"\"Scrub numeric-looking data cells from placeholder tables.\"\"\"\n    numeric = re.compile(\n        r'^\\s*(?:\\\\textbf\\{)?[-+]?\\d[\\d,]*(?:\\.\\d+)?\\s*(?:%|ms|s|x|MB|GB|points?)?(?:\\})?\\s*$',\n        re.I,\n    )\n    out = []\n    in_tabular = False\n    after_midrule = False\n    for line in tex.splitlines():\n        if r'\\begin{tabular}' in line:\n            in_tabular = True\n            after_midrule = False\n            out.append(line)\n            continue\n        if in_tabular and r'\\end{tabular}' in line:\n            in_tabular = False\n            after_midrule = False\n            out.append(line)\n            continue\n        if in_tabular and r'\\midrule' in line:\n            after_midrule = True\n            out.append(line)\n            continue\n        if in_tabular and after_midrule and '&' in line and r'\\bottomrule' not in line:\n            suffix = r' \\\\' if line.rstrip().endswith(r'\\\\') else ''\n            row = line.rstrip()\n            if suffix:\n                row = row[:-2].rstrip()\n            cells = [cell.strip() for cell in row.split('&')]\n            if len(cells) > 1:\n                cells = [cells[0], *('---' if numeric.match(cell) else cell for cell in cells[1:])]\n                indent = re.match(r'^\\s*', line).group(0)\n                line = indent + ' & '.join(cells) + suffix\n        out.append(line)\n    return '\\n'.join(out)\n\n# 6. If still empty, fail loudly. Quality-first paper generation\n# must not disguise a missing manuscript as a degraded PDF.\nif not tex_body:\n    print('COMPILE_FAILED: MANUSCRIPT_TEX block missing; refusing to create degraded PDF')\n    print('PACKAGE_PREVIEW:')\n    print(pkg[:2000])\n    sys.exit(1)\n\n# 7. Auto-wrap if the LLM gave a body fragment but no \\documentclass.\nif '\\\\documentclass' not in tex_body:\n    tex_body = (\n        r\"\\documentclass{article}\" \"\\n\"\n        r\"\\usepackage{xeCJK}\" \"\\n\"\n        r\"\\usepackage{graphicx}\\usepackage{booktabs}\\usepackage{amsmath}\\usepackage{hyperref}\" \"\\n\"\n        r\"\\begin{document}\" \"\\n\"\n        + tex_body + \"\\n\"\n        r\"\\bibliographystyle{plain}\" \"\\n\"\n        r\"\\bibliography{references}\" \"\\n\"\n        r\"\\end{document}\" \"\\n\"\n    )\n\n# 8. Auto-add xeCJK if the body contains CJK chars but doesn't load it.\nif re.search(r'[一-鿿]', tex_body) and 'xeCJK' not in tex_body:\n    tex_body = tex_body.replace(\n        r'\\documentclass{article}',\n        r'\\documentclass{article}' + '\\n' + r'\\usepackage{xeCJK}',\n        1,\n    )\n\ntex_body = scrub_placeholder_table_cells(tex_body)\n\npaper = Path('paper'); paper.mkdir(exist_ok=True)\n(paper \/ 'paper.tex').write_text(tex_body, encoding='utf-8')\n(paper \/ 'references.bib').write_text(bib, encoding='utf-8')\n\nlogs = []\nfor cmd in (\n    ['xelatex','-interaction=nonstopmode','paper.tex'],\n    ['bibtex','paper'],\n    ['xelatex','-interaction=nonstopmode','paper.tex'],\n    ['xelatex','-interaction=nonstopmode','paper.tex'],\n):\n    r = subprocess.run(cmd, cwd='paper', capture_output=True, text=True)\n    logs.append(f\"--- {' '.join(cmd)} (rc={r.returncode}) ---\")\n\npdf = (paper \/ 'paper.pdf').resolve()\nif pdf.is_file():\n    log_text = (paper \/ 'paper.log').read_text(encoding='utf-8', errors='ignore') if (paper \/ 'paper.log').is_file() else ''\n    pm = re.search(r'Output written on .+?\\((\\d+) pages?', log_text)\n    pages = pm.group(1) if pm else '?'\n    print(f'PDF_PATH: {pdf}')\n    print(f'PDF_PAGES: {pages}')\n    print(f'PDF_BYTES: {pdf.stat().st_size}')\n    print(f'TEX_BYTES: {(paper \/ \"paper.tex\").stat().st_size}')\n    print(f'BIB_BYTES: {(paper \/ \"references.bib\").stat().st_size}')\nelse:\n    tail = '\\n'.join(logs[-3:])\n    # Dump the last 80 lines of paper.log so the failure mode is visible.\n    log_text = (paper \/ 'paper.log').read_text(encoding='utf-8', errors='ignore') if (paper \/ 'paper.log').is_file() else ''\n    log_tail = '\\n'.join(log_text.splitlines()[-80:])\n    print(f'COMPILE_FAILED:\\n{tail}\\n\\n=== paper.log tail ===\\n{log_tail}')\n    sys.exit(1)\nPY\n",
                    "timeout": 120,
                    "workdir": "{{ inputs.workspace_dir }}"
                },
                "depends_on": [
                    "latex_sanitizer",
                    "consistency_pass",
                    "assemble_manuscript_tex",
                    "final_manuscript_package"
                ],
                "tool_allowlist": [
                    "exec_command"
                ]
            },
            {
                "id": "publish_pdf",
                "kind": "tool_call",
                "tool": "publish_artifact",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract",
                "label": "发布 PDF",
                "label_en": "Publish PDF",
                "tool_args": {
                    "mime": "application\/pdf",
                    "name": "paper.pdf",
                    "path": "paper\/paper.pdf"
                },
                "depends_on": [
                    "compile_pdf"
                ],
                "tool_allowlist": [
                    "publish_artifact"
                ]
            },
            {
                "id": "deliver_paper",
                "kind": "llm_chat",
                "when": "'PAPER_MODE: FULL_MANUSCRIPT' in outputs.paper_contract or 'PAPER_MODE: COMPACT_SKELETON' in outputs.paper_contract or 'PAPER_MODE: REPAIR_EXISTING' in outputs.paper_contract",
                "with": {
                    "task": "Produce the user-facing delivery message. Confirm the PDF\nis ready, name its location, page count, citation summary,\nand list any open warnings from the citation audit. Keep\nthe message under 200 words.\n\nUSER_LANGUAGE: {{ inputs.get('user_language', 'zh' if (inputs.user_message | contains_cjk) else 'en') }}\n\nLanguage rules:\n- If USER_LANGUAGE is en, write English only. Do not include Chinese\n  headings, labels, warnings, or bilingual labels.\n- If USER_LANGUAGE is zh, write Chinese only. Do not include English\n  headings except literal file paths, artifact IDs, and citation keys.\n- Do not copy warning prose from intermediate audit text; translate\n  any warning into the selected USER_LANGUAGE.\n\nOriginal request:\n{{ inputs.user_message | xml_escape | truncate(400) }}\n\nPDF compile result (paths are absolute):\n{{ outputs.compile_pdf | truncate(800) }}\n\nArtifact publication result:\n{{ outputs.publish_pdf | truncate(800) }}\n\nCitation audit summary tail:\n{{ outputs.citation_map | truncate(2000) }}\n\n{% if inputs.get('user_language') == 'zh' or (inputs.user_message | contains_cjk) %}\nFormat:\n📄 论文已生成\n\n- PDF: <absolute path or artifact id>\n- 页数: <N>\n- 引用: <total \/ strong \/ weak \/ invalid \/ unused>\n- 备注: <one line about figures, tables, analysis dimensions>\n\nIf the audit shows INVALID > 0, prefix the message with\n\"⚠️ 注意: <N> 处引用未在 bib 中，建议重新生成\" and list the offending\ncite keys.\n{% else %}\nFormat:\n📄 Paper compiled\n\n- PDF: <absolute path or artifact id>\n- Pages: <N>\n- Citations: <total \/ strong \/ weak \/ invalid \/ unused>\n- Notes: <one line about figures, tables, analysis dimensions>\n\nIf the audit shows INVALID > 0, prefix the message with\n\"⚠️ Warning: <N> citation keys are missing from references.bib; regenerate\nor repair the bibliography\" and list the offending cite keys.\n{% endif %}",
                    "system": "You write a one-paragraph delivery note for a compiled academic paper. Output is concise — no LaTeX source, no markdown fences. Obey USER_LANGUAGE strictly: en means English only; zh means Chinese only."
                },
                "label": "论文交付",
                "label_en": "Paper delivery",
                "depends_on": [
                    "final_manuscript_package",
                    "compile_pdf",
                    "publish_pdf",
                    "citation_map"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when the current user asks to draft, repair, compile, or produce an academic\/research paper or LaTeX manuscript. It uses multi-skill orchestration for manuscript workflows that need source search, citation planning, experiment or figure\/table placeholders, drafting, length checks, citation integrity, and LaTeX\/PDF compilation. Ordinary paper requests use a compact draft path; explicit full\/PDF\/long-form requests use the full manuscript path. Do not use it for web research reports, slide decks, document decisions, or generic plotting.",
    "policy_tags": [
        "no-fabricated-citations",
        "no-fabricated-results"
    ],
    "eval_prompts": [
        {
            "name": "paper-write-baseline",
            "prompt": "Draft a compact research-paper outline with citation status and known gaps for a supplied topic.",
            "rubric": [
                "Manuscript or repair output",
                "Citation and source status",
                "Known gaps",
                "Next validation step"
            ]
        }
    ],
    "meta_priority": 50,
    "final_text_mode": "step:deliver_paper",
    "output_contract": {
        "artifacts": [
            {
                "name": "paper_artifact",
                "required": false
            }
        ],
        "unverified": [
            "Claims without supplied data or verified citations."
        ],
        "assumptions": [
            "Draft mode and artifact generation follow the user's explicit request."
        ],
        "required_sections": [
            "Manuscript or repair output",
            "Citation and source status",
            "Known gaps",
            "Next validation step"
        ],
        "append_to_final_text": false
    },
    "preference_keys": [
        "preferred_language",
        "citation_style"
    ],
    "request_template": {
        "fields": [
            {
                "name": "paper_topic_or_manuscript",
                "label_en": "Paper topic or manuscript",
                "label_zh": "论文主题或稿件",
                "required": true
            },
            {
                "name": "mode",
                "default": "compact draft unless full\/PDF\/long-form is explicit",
                "label_en": "Mode",
                "label_zh": "模式",
                "required": false,
                "default_en": "compact draft unless full\/PDF\/long-form is explicit",
                "default_zh": "默认紧凑草稿；仅在明确要求完整\/PDF\/长文时生成完整稿件"
            },
            {
                "name": "target_venue_or_style",
                "label_en": "Target venue or style",
                "label_zh": "目标会议\/期刊或风格",
                "required": false
            },
            {
                "name": "citation_requirements",
                "label_en": "Citation requirements",
                "label_zh": "引用要求",
                "required": false
            },
            {
                "name": "audience",
                "default": "academic reader or target venue",
                "label_en": "Audience",
                "label_zh": "受众",
                "required": false,
                "default_en": "academic reader or target venue",
                "default_zh": "学术读者或目标投稿 venue"
            },
            {
                "name": "language",
                "default": "match the user's language",
                "label_en": "Output language",
                "label_zh": "输出语言",
                "required": false,
                "default_en": "match the user's language",
                "default_zh": "跟随用户语言"
            }
        ],
        "outcome": "Academic manuscript draft or repair pass with citation and compilation checks as requested.",
        "outcome_en": "Academic manuscript draft or repair pass with citation and compilation checks as requested.",
        "outcome_zh": "按需生成或修订学术稿件，并检查引用与编译状态。",
        "assumptions": [
            "Do not fabricate citations or experimental results.",
            "Use compact output unless the request explicitly asks for full manuscript artifacts."
        ],
        "assumptions_en": [
            "Do not fabricate citations or experimental results.",
            "Use compact output unless the request explicitly asks for full manuscript artifacts."
        ],
        "assumptions_zh": [
            "不编造引用或实验结果。",
            "除非用户明确要求完整稿件产物，否则使用紧凑输出。"
        ]
    }
}

{
    "kind": "meta",
    "name": "meta-short-drama",
    "always": false,
    "metadata": {
        "opensquilla": {
            "risk": "high",
            "capabilities": [
                "network-read",
                "filesystem-write",
                "process-control"
            ],
            "composition_skills": [
                "ai-video-script",
                "nano-banana-pro",
                "seedance-2-prompt",
                "video-still-animator",
                "video-merger",
                "srt-from-script",
                "subtitle-burner",
                "title-card-image",
                "text-file-read"
            ]
        }
    },
    "triggers": [
        "生成短剧",
        "生成一个短剧",
        "生成一段短剧",
        "做一个AI短剧",
        "帮我做一个短剧",
        "三分镜短剧",
        "短视频分镜成片",
        "分镜成片",
        "generate a short drama",
        "generate short drama",
        "make a short drama from",
        "topic to short drama mp4",
        "shot list to final mp4"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "intake_extract",
                "kind": "llm_chat",
                "with": {
                    "task": "Read the request and emit exactly this 7-line block, in this\norder, with no extra commentary:\n\nTOPIC: <one short line — the actual story\/product topic>\nRENDER_STYLE: <render aesthetic, one line in user's language>\nAUTO_FILLED_RENDER_STYLE: <yes|no>\nIDENTITY_ANCHOR: <one line in user's language describing main character(s)>\nAUTO_FILLED_IDENTITY_ANCHOR: <yes|no>\nN_SHOTS: <integer 1..10, default 5>\nAUTO_FILLED_N_SHOTS: <yes|no>\n\nRules:\n- Detect dominant language of the request. Use that language for\n  RENDER_STYLE and IDENTITY_ANCHOR. Downstream models accept\n  Chinese natively (seedance is Chinese-first).\n- If user named a render style verbatim → copy it, AUTO_FILLED_RENDER_STYLE: no.\n- Else INFER a render style from the TOPIC's genre, era, and\n  tone — DO NOT default to anime. Pick whichever of these\n  best fits the story you just read; fall through to a fresh\n  descriptor if none match exactly. Use the user's language.\n    * 现代职场 \/ 都市爽剧 \/ 商战 \/ 反转 \/ corporate drama →\n        电影级写实, 真实摄影, 戏剧化强光对比, 高对比度色调\n        \/ Cinematic realism, dramatic high-contrast lighting\n    * 古风 \/ 武侠 \/ 仙侠 \/ 宫廷 \/ wuxia \/ xianxia →\n        水墨风, 中国传统工笔画, 柔和留白构图\n        \/ Ink-wash painting, traditional Chinese gongbi style\n    * 校园 \/ 青春 \/ 恋爱 \/ 治愈 \/ slice-of-life \/ romance →\n        日系胶片质感, 柔和自然光, 浅景深, 温暖调色\n        \/ Japanese film aesthetic, soft natural light, warm grade\n    * 科幻 \/ 赛博朋克 \/ 未来 \/ sci-fi \/ cyberpunk →\n        赛博朋克霓虹, 体积光雾气, 高对比反射, 未来感\n        \/ Cyberpunk neon, volumetric haze, future-noir\n    * 恐怖 \/ 悬疑 \/ 惊悚 \/ horror \/ thriller \/ noir →\n        低调照明, 高反差暗调, 电影黑色风格\n        \/ Low-key lighting, high-contrast noir, cinematic shadow\n    * 童话 \/ 绘本 \/ 儿童 \/ fairytale \/ picture-book \/ kids →\n        水彩绘本插画, 柔和纸面纹理, 暖色调\n        \/ Watercolour storybook, soft paper texture, warm palette\n    * 商品 \/ 广告 \/ 带货 \/ product \/ commercial →\n        影棚布光, 浅景深产品特写, 干净背景\n        \/ Studio lighting, hero-product close-up, clean background\n    * 美食 \/ 烹饪 \/ food \/ cooking →\n        顶光美食摄影, 自然质感, 浅景深\n        \/ Top-down food photography, natural texture, shallow DOF\n    * 科普 \/ 教学 \/ 信息图 \/ explainer \/ educational →\n        扁平信息图风格, 简洁配色, 平面构图\n        \/ Flat infographic style, clean palette, geometric layout\n    * 卡通 \/ 动画 \/ 二次元 \/ 萌系 — only when the user really\n      wants anime → 2D 动漫插画, 扁平上色, 柔和赛璐璐阴影\n                      \/ 2D anime illustration, flat cel-shading\n    * none of the above → write ONE descriptive line that\n      matches the topic's mood (NOT anime by default). Examples:\n        documentary realism \/ oil-painting cinematic \/ vintage\n        super-8 grain \/ minimalist black-and-white photography.\n  AUTO_FILLED_RENDER_STYLE: yes\n- If user described main character(s) with at least\n  ethnicity + age + hair + outfit → summarise ≤40 words,\n  AUTO_FILLED_IDENTITY_ANCHOR: no.\n- Else invent ONE or TWO original characters fitting the TOPIC.\n- If user named shot count (3 个分镜 \/ \"5 shots\" \/ etc.) → use it\n  clamped 1..10, AUTO_FILLED_N_SHOTS: no.\n- Else default N_SHOTS: 5, AUTO_FILLED_N_SHOTS: yes.\n- Never ask the user a question. The user reviews in step 3.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1500) }}\n",
                    "system": "Extract or invent a short-drama intake contract. Match the user's language for RENDER_STYLE \/ IDENTITY_ANCHOR. Be conservative — pick safe defaults rather than asking the user."
                },
                "label": "需求提取",
                "label_en": "Requirement extraction"
            },
            {
                "id": "script_draft",
                "kind": "agent",
                "with": {
                    "task": "Generate a strict-format short-drama shooting script following\nai-video-script's SKILL.md OUTPUT FORMAT section. Use the\nN_SHOTS value from the intake contract below (clamp 1..10).\nDefault DURATION_S total: 50 (~10s per shot for the default 5\nshots). ASPECT_RATIO: 9:16.\n\nOutput style: plain text only. No emoji, no decorative symbols.\nDo not call publish_artifact or any other tool. The meta-skill\ncaptures this step's final assistant text directly, so your final\nmessage must contain the complete script itself, not a file link,\nartifact marker, or \"[Used tool: ...]\" placeholder.\n\nLanguage: match the user's request language for every field.\nBoth downstream models accept CJK natively — do NOT translate\nChinese stories into English.\n\nIDENTITY_ANCHOR and RENDER_STYLE below are caller-supplied —\npaste them byte-for-byte into every shot's IMAGE_PROMPT and\nVIDEO_PROMPT. Do not paraphrase or invent alternates.\n\nIntake contract:\n{{ outputs.intake_extract | truncate(1500) }}\n\nUser original request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nEmit OVERVIEW.IDENTITY_ANCHOR, OVERVIEW.RENDER_STYLE, and\nOVERVIEW.N_SHOTS lines so downstream steps can re-extract them.\n"
                },
                "label": "剧本初稿",
                "skill": "ai-video-script",
                "label_en": "Draft script",
                "depends_on": [
                    "intake_extract"
                ]
            },
            {
                "id": "script_save_draft",
                "kind": "tool_call",
                "tool": "write_file",
                "label": "保存初稿",
                "label_en": "Save draft",
                "tool_args": {
                    "path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/script.txt",
                    "content": "{{ outputs.script_draft }}"
                },
                "depends_on": [
                    "script_draft"
                ],
                "tool_allowlist": [
                    "write_file"
                ]
            },
            {
                "id": "review_gate",
                "kind": "user_input",
                "label": "审查门禁",
                "clarify": {
                    "mode": "form",
                    "intro": "脚本就绪。下面是脚本预览 + 我对风格\/角色\/分镜数做的假设\n(标 AUTO_FILLED: yes 的项是我替你填的,你可以改)。\n\n脚本草稿已存到本次运行目录的 script.txt —— 想直接改文件也行,\n下一步会重新读盘,你的手动编辑会一起带进去。\n\n你怎么回都行 —— 不用按固定格式:\n  - 满意就直接说 \"ok\" \/ \"继续\" \/ \"proceed\"\n  - 想换风格 → 写一句新的 RENDER_STYLE\n  - 想换角色 → 写新的 IDENTITY_ANCHOR\n  - 想改分镜数 → 直接说 \"5 个分镜\" \/ \"改成 7 镜头\"\n  - 想改某镜内容 → 直接说 \"镜头2节奏快点\" \/ \"shot 3 换成屋顶场景\"\n  - 不想做了 → 说 \"取消\" \/ \"cancel\" \/ \"停\"\n\n预估成本(选继续才会发生):\n  - N 张镜头图 + 1 张全角色参考图 (nano-banana-pro)  ≈ N × $0.05 + $0.05-$0.10\n  - N 段视频 (seedance-2.0)   ≈ $0.15\/s × 总时长\n    (脚本里每镜 DURATION_S 决定时长)\n  - 封面 + 结尾卡 (本地 Pillow + ffmpeg,免费)\n  - ffmpeg 拼接 + 烧字幕\n  合计随 N_SHOTS 与总时长缩放。\n\n=== 我做的假设 ===\n{{ outputs.intake_extract | truncate(800) }}\n\n=== 脚本草稿 ===\n{{ outputs.script_draft | truncate(3500) }}\n",
                    "fields": [
                        {
                            "name": "review",
                            "type": "string",
                            "prompt": "用户对脚本草稿的整段回复 — 直接把用户说的所有文字原样\n放进这个字段,不要总结、不要重写、不要解释。这是一个\ncatch-all 字段:任何同意\/拒绝\/修改意见\/吐槽\/闲聊都属于这里。\nThe user's verbatim reply about the script draft. Copy the\nuser's entire reply text into this single field — do not\nsummarise, paraphrase, translate, or split it. This is a\ncatch-all: approvals, rejections, edits, off-topic remarks\nall belong here. If the user's reply is empty or pure\nwhitespace, emit \"(empty)\" so the field always has a value.\n",
                            "required": true,
                            "max_chars": 4000,
                            "prompt_en": "The user's verbatim reply about the script draft. Copy the entire reply into this single field; do not summarize, paraphrase, translate, or split it. Approvals, rejections, edits, off-topic remarks, and empty replies all belong here.\n",
                            "prompt_zh": "用户对脚本草稿的整段回复。原样放进这个字段，不要总结、不要重写、不要解释。任何同意、拒绝、修改意见、吐槽或闲聊都属于这里。\n"
                        }
                    ],
                    "intro_en": "The script is ready. Below is the script preview plus the assumptions I made about style, character identity, and shot count.\n\nItems marked AUTO_FILLED: yes were filled conservatively and can be changed. The draft script was saved to script.txt in this run directory; if you edit that file directly, the next step will reread it and include your manual edits.\n\nReply naturally: say \"continue\" if it looks good, describe any style, character, shot-count, or shot-level changes, or say \"cancel\" to stop.\n\nEstimated media cost only happens if you continue, and mainly scales with shot count and total duration.\n\n=== Assumptions I made ===\n{{ outputs.intake_extract | truncate(800) }}\n\n=== Script draft ===\n{{ outputs.script_draft | truncate(3500) }}\n",
                    "intro_zh": "脚本就绪。下面是脚本预览，以及我对风格、角色和分镜数做的假设。\n\n标 AUTO_FILLED: yes 的项是我替你填的，你可以改。脚本草稿已存到本次运行目录的 script.txt；如果你直接改文件，下一步会重新读盘并带入修改。\n\n你怎么回都行：满意就说“继续”；想换风格、角色、分镜数或某个镜头，直接说你的修改；不想做了就说“取消”。\n\n预估成本只会在你选择继续后发生，主要随镜头数和总时长变化。\n\n=== 我做的假设 ===\n{{ outputs.intake_extract | truncate(800) }}\n\n=== 脚本草稿 ===\n{{ outputs.script_draft | truncate(3500) }}\n",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "cancel",
                        "取消",
                        "算了",
                        "停止",
                        "stop",
                        "abort"
                    ]
                },
                "label_en": "Review gate",
                "depends_on": [
                    "script_save_draft",
                    "script_draft",
                    "intake_extract"
                ]
            },
            {
                "id": "review_normalize",
                "kind": "llm_chat",
                "with": {
                    "task": "Parse the user's free-form review of the script draft and emit\nexactly this block:\n\nDECISION: <proceed|cancel>\nHAS_OVERRIDES: <yes|no>\nNEW_RENDER_STYLE: <new one-line value, or \"unchanged\">\nNEW_IDENTITY_ANCHOR: <new one-line value, or \"unchanged\">\nNEW_N_SHOTS: <integer 1..10, or \"unchanged\">\nNEW_NOTES: <any other adjustments to story \/ shots \/ voiceover, or \"unchanged\">\n\nRules:\n- DECISION: cancel only on explicit cancel\/取消\/算了\/停 words.\n- DECISION: proceed otherwise (approvals AND adjustments).\n- HAS_OVERRIDES: yes if ANY of NEW_RENDER_STYLE \/\n  NEW_IDENTITY_ANCHOR \/ NEW_N_SHOTS \/ NEW_NOTES differs from\n  \"unchanged\".\n- NEW_RENDER_STYLE \/ NEW_IDENTITY_ANCHOR \/ NEW_NOTES: use the\n  same language as the user's reply.\n- NEW_N_SHOTS: extract integer (e.g. \"改成 5 镜头\" → 5).\n  Clamp 1..10. Else \"unchanged\".\n\nFree-form user review:\n{{ inputs.get('collected', {}).get('review_gate', {}) | tojson | truncate(2200) }}\n\nOriginal assumptions (for delta detection):\n{{ outputs.intake_extract | truncate(800) }}\n",
                    "system": "Emit a strict 6-line block. No commentary outside it."
                },
                "label": "审查归一",
                "label_en": "Review normalization",
                "depends_on": [
                    "review_gate"
                ]
            },
            {
                "id": "script_reread",
                "kind": "skill_exec",
                "with": {
                    "input": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/script.txt"
                },
                "label": "剧本复读",
                "skill": "text-file-read",
                "label_en": "Script reread",
                "depends_on": [
                    "review_gate",
                    "script_save_draft"
                ]
            },
            {
                "id": "script_revised",
                "kind": "agent",
                "when": "'DECISION: proceed' in outputs.review_normalize and 'HAS_OVERRIDES: yes' in outputs.review_normalize",
                "with": {
                    "task": "Re-draft the script applying the user's overrides. Keep the\nsame OUTPUT FORMAT as ai-video-script's SKILL.md. If NEW_N_SHOTS\nis an integer, use exactly that many shot blocks (1..10).\nOtherwise keep the original N_SHOTS.\n\nOutput style: plain text only. No emoji.\nLanguage: keep the user's original request language.\n\nApply overrides in priority: NEW_NOTES → NEW_N_SHOTS →\nNEW_RENDER_STYLE → NEW_IDENTITY_ANCHOR. \"unchanged\" fields\ninherit from the previous script verbatim.\n\nPrevious script (re-read from disk — if the user hand-edited\nscript.txt during review, those edits are already baked in\nhere, so preserve them):\n{{ outputs.script_reread | truncate(8000) }}\n\nParsed overrides:\n{{ outputs.review_normalize | truncate(1500) }}\n\nUser original request:\n{{ inputs.user_message | xml_escape | truncate(800) }}\n"
                },
                "label": "剧本修订",
                "skill": "ai-video-script",
                "label_en": "Script revision",
                "depends_on": [
                    "review_normalize",
                    "script_reread"
                ]
            },
            {
                "id": "final_script",
                "kind": "llm_chat",
                "with": {
                    "task": "If a revised script block is present below, echo it verbatim.\nOtherwise echo the re-read script verbatim (this preserves any\nhand-edits the user made to script.txt during review).\n\nREVISED (may be empty):\n{{ outputs.get('script_revised', '') | truncate(8000) }}\n\nRE-READ FROM DISK:\n{{ outputs.script_reread | truncate(8000) }}\n",
                    "system": "Echo one of two inputs verbatim. No commentary. No new content."
                },
                "label": "最终剧本",
                "label_en": "Final script",
                "depends_on": [
                    "review_normalize",
                    "script_reread",
                    "script_revised"
                ]
            },
            {
                "id": "script_save",
                "kind": "tool_call",
                "tool": "write_file",
                "label": "保存剧本",
                "label_en": "Save script",
                "tool_args": {
                    "path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/script.txt",
                    "content": "{{ outputs.final_script }}"
                },
                "depends_on": [
                    "final_script"
                ],
                "tool_allowlist": [
                    "write_file"
                ]
            },
            {
                "id": "title_extract",
                "kind": "llm_chat",
                "with": {
                    "task": "From the script, output exactly the value after \"TITLE:\"\ninside the \"=== OVERVIEW ===\" block. Single line.\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "标题提取",
                "label_en": "Title extraction",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "subtitle_extract",
                "kind": "llm_chat",
                "with": {
                    "task": "Compose a short subtitle for the cover card describing this\ndrama in 5-12 characters (or 2-4 English words). Match the\nscript's language. Examples:\n  Chinese script → \"AI 短剧 · 30 秒\"\n  English script → \"AI Short Drama · 30s\"\n\nScript (read OVERVIEW.TITLE \/ DURATION_S \/ AUDIENCE):\n{{ outputs.final_script | truncate(2000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "字幕提取",
                "label_en": "Subtitle extraction",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "ending_text_extract",
                "kind": "llm_chat",
                "with": {
                    "task": "Output the appropriate ending-card text. Single line, no commentary.\n  Chinese script  → 完\n  English script  → THE END\n  Other languages → THE END\n\nScript (sample to detect language):\n{{ outputs.final_script | truncate(1500) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "结尾文案",
                "label_en": "Closing copy",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "reference_prompt_extract",
                "kind": "llm_chat",
                "with": {
                    "task": "Build a single-line image prompt for a full-cast identity\nreference card. The picture must show EVERY named character\nthat appears in ANY shot of the script (NOT just the\nOVERVIEW.IDENTITY_ANCHOR anchors — supporting cast, cameo\ncharacters, anyone the script mentions by name in any SHOT\nblock also belongs here), standing together in a neutral\nlineup against a neutral backdrop. The downstream video model\nuses this image as the universal identity anchor for every\nshot.\n\nProcedure (do these silently in your head; only emit the final\nsingle-line prompt):\n\n1. Read the entire script. Enumerate every distinct named\n   character that appears in ANY SHOT_N block's IMAGE_PROMPT\n   or VIDEO_PROMPT. Include characters who appear in only one\n   shot. Deduplicate by name. Let N be the count.\n2. For each character, write the most complete canonical\n   attribute string the script gives them (name, age,\n   ethnicity, hair, outfit, distinguishing accessory). Pull\n   missing fields from OVERVIEW.IDENTITY_ANCHOR if needed.\n3. Compose the final prompt as a single line in this exact\n   order:\n\n     <char 1 description>; <char 2 description>; ...; <char N description>, ALL <N> characters standing side by side in a horizontal full-body group lineup, every character clearly visible from head to toe, evenly spaced across frame, wide-angle group photo, neutral studio lighting, neutral light grey backdrop, no props, no background scene, group portrait composition, <OVERVIEW.RENDER_STYLE verbatim>, --ar 9:16\n\n   - Use ; (semicolon) BETWEEN characters, exactly as in the\n     examples above.\n   - State the integer N explicitly inside \"ALL <N> characters\".\n   - If N = 1, still say \"ALL 1 character\" and drop the\n     \"side by side \/ horizontal lineup\" phrasing — write\n     \"single-character full-body portrait\" instead.\n\nOutput a single line. No quotes. No commentary outside the\nprompt itself.\n\nScript (READ THE FULL SCRIPT, including every SHOT_N block,\nnot just OVERVIEW):\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "参考提示",
                "label_en": "Reference prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "reference_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "model": "google\/gemini-3-pro-image-preview",
                    "prompt": "{{ outputs.reference_prompt_extract | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3.1-flash-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "参考图",
                "skill": "nano-banana-pro",
                "label_en": "Reference image",
                "depends_on": [
                    "reference_prompt_extract",
                    "review_normalize"
                ]
            },
            {
                "id": "cover_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "text": "{{ outputs.title_extract | truncate(40) }}",
                    "width": 720,
                    "height": 1280,
                    "output": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/0_cover.png",
                    "subtitle": "{{ outputs.subtitle_extract | truncate(40) }}",
                    "font_size": 80,
                    "background": "#101018",
                    "text_color": "#ffffff",
                    "subtitle_size": 32
                },
                "label": "封面图",
                "skill": "title-card-image",
                "label_en": "Cover image",
                "depends_on": [
                    "title_extract",
                    "subtitle_extract",
                    "review_normalize"
                ]
            },
            {
                "id": "cover_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": 2,
                    "zoom_rate": 0.0008,
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/0_cover.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/0_cover.mp4"
                },
                "label": "封面视频",
                "skill": "video-still-animator",
                "label_en": "Cover video",
                "depends_on": [
                    "cover_image",
                    "review_normalize"
                ]
            },
            {
                "id": "shot1_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_1 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 1):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头1图提示",
                "label_en": "Shot 1 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot1_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_1 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头1视频提示",
                "label_en": "Shot 1 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot1_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_1 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头1时长",
                "label_en": "Shot 1 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot2_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_2 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 2):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头2图提示",
                "label_en": "Shot 2 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot2_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_2 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头2视频提示",
                "label_en": "Shot 2 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot2_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_2 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头2时长",
                "label_en": "Shot 2 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot3_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_3 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 3):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头3图提示",
                "label_en": "Shot 3 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot3_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_3 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头3视频提示",
                "label_en": "Shot 3 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot3_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_3 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头3时长",
                "label_en": "Shot 3 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot4_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_4 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 4):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头4图提示",
                "label_en": "Shot 4 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot4_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_4 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头4视频提示",
                "label_en": "Shot 4 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot4_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_4 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头4时长",
                "label_en": "Shot 4 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot5_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_5 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 5):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头5图提示",
                "label_en": "Shot 5 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot5_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_5 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头5视频提示",
                "label_en": "Shot 5 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot5_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_5 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头5时长",
                "label_en": "Shot 5 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot6_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_6 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 6):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头6图提示",
                "label_en": "Shot 6 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot6_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_6 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头6视频提示",
                "label_en": "Shot 6 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot6_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_6 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头6时长",
                "label_en": "Shot 6 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot7_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_7 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 7):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头7图提示",
                "label_en": "Shot 7 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot7_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_7 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头7视频提示",
                "label_en": "Shot 7 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot7_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_7 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头7时长",
                "label_en": "Shot 7 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot8_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_8 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 8):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头8图提示",
                "label_en": "Shot 8 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot8_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_8 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头8视频提示",
                "label_en": "Shot 8 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot8_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_8 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头8时长",
                "label_en": "Shot 8 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot9_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_9 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 9):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头9图提示",
                "label_en": "Shot 9 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot9_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_9 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头9视频提示",
                "label_en": "Shot 9 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot9_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_9 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头9时长",
                "label_en": "Shot 9 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot10_img_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_10 ===\" block:\n  output exactly the value after \"IMAGE_PROMPT:\" inside that block.\n  Single line, no quotes, no label.\nIf it does NOT (because N_SHOTS < 10):\n  output exactly the literal sentinel: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头10图提示",
                "label_en": "Shot 10 image prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot10_vid_prompt",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_10 ===\" block:\n  output exactly the value after \"VIDEO_PROMPT:\" inside that block.\n  Single line.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return one line of text. No quotes, no prefix, no commentary."
                },
                "label": "镜头10视频提示",
                "label_en": "Shot 10 video prompt",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot10_duration",
                "kind": "llm_chat",
                "with": {
                    "task": "If the script contains a \"=== SHOT_10 ===\" block:\n  output exactly the integer after \"DURATION_S:\" inside that\n  block, clamped to [3, 15]. Digits only, no units.\nIf it does NOT: output exactly: __SHOT_ABSENT__\n\nScript:\n{{ outputs.final_script | truncate(8000) }}\n",
                    "system": "Return exactly one integer or the literal __SHOT_ABSENT__. No commentary."
                },
                "label": "镜头10时长",
                "label_en": "Shot 10 duration",
                "depends_on": [
                    "final_script"
                ]
            },
            {
                "id": "shot1_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot1_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot1_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/1_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头1图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 1 image",
                "depends_on": [
                    "shot1_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot1_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot1_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot1_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot1_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/1_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/1_shot.png"
                },
                "label": "镜头1视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 1 video",
                "depends_on": [
                    "shot1_vid_prompt",
                    "shot1_duration",
                    "reference_image",
                    "shot1_image",
                    "review_normalize"
                ],
                "on_failure": "shot1_video_fallback"
            },
            {
                "id": "shot1_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot1_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/1_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/1_shot.mp4"
                },
                "label": "镜头1视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 1 video fallback"
            },
            {
                "id": "shot2_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot2_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot2_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/2_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头2图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 2 image",
                "depends_on": [
                    "shot2_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot2_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot2_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot2_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot2_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/2_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/2_shot.png"
                },
                "label": "镜头2视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 2 video",
                "depends_on": [
                    "shot2_vid_prompt",
                    "shot2_duration",
                    "reference_image",
                    "shot2_image",
                    "review_normalize"
                ],
                "on_failure": "shot2_video_fallback"
            },
            {
                "id": "shot2_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot2_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/2_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/2_shot.mp4"
                },
                "label": "镜头2视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 2 video fallback"
            },
            {
                "id": "shot3_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot3_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot3_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/3_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头3图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 3 image",
                "depends_on": [
                    "shot3_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot3_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot3_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot3_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot3_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/3_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/3_shot.png"
                },
                "label": "镜头3视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 3 video",
                "depends_on": [
                    "shot3_vid_prompt",
                    "shot3_duration",
                    "reference_image",
                    "shot3_image",
                    "review_normalize"
                ],
                "on_failure": "shot3_video_fallback"
            },
            {
                "id": "shot3_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot3_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/3_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/3_shot.mp4"
                },
                "label": "镜头3视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 3 video fallback"
            },
            {
                "id": "shot4_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot4_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot4_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/4_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头4图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 4 image",
                "depends_on": [
                    "shot4_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot4_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot4_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot4_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot4_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/4_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/4_shot.png"
                },
                "label": "镜头4视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 4 video",
                "depends_on": [
                    "shot4_vid_prompt",
                    "shot4_duration",
                    "reference_image",
                    "shot4_image",
                    "review_normalize"
                ],
                "on_failure": "shot4_video_fallback"
            },
            {
                "id": "shot4_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot4_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/4_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/4_shot.mp4"
                },
                "label": "镜头4视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 4 video fallback"
            },
            {
                "id": "shot5_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot5_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot5_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/5_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头5图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 5 image",
                "depends_on": [
                    "shot5_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot5_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot5_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot5_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot5_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/5_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/5_shot.png"
                },
                "label": "镜头5视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 5 video",
                "depends_on": [
                    "shot5_vid_prompt",
                    "shot5_duration",
                    "reference_image",
                    "shot5_image",
                    "review_normalize"
                ],
                "on_failure": "shot5_video_fallback"
            },
            {
                "id": "shot5_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot5_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/5_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/5_shot.mp4"
                },
                "label": "镜头5视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 5 video fallback"
            },
            {
                "id": "shot6_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot6_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot6_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/6_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头6图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 6 image",
                "depends_on": [
                    "shot6_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot6_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot6_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot6_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot6_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/6_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/6_shot.png"
                },
                "label": "镜头6视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 6 video",
                "depends_on": [
                    "shot6_vid_prompt",
                    "shot6_duration",
                    "reference_image",
                    "shot6_image",
                    "review_normalize"
                ],
                "on_failure": "shot6_video_fallback"
            },
            {
                "id": "shot6_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot6_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/6_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/6_shot.mp4"
                },
                "label": "镜头6视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 6 video fallback"
            },
            {
                "id": "shot7_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot7_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot7_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/7_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头7图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 7 image",
                "depends_on": [
                    "shot7_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot7_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot7_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot7_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot7_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/7_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/7_shot.png"
                },
                "label": "镜头7视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 7 video",
                "depends_on": [
                    "shot7_vid_prompt",
                    "shot7_duration",
                    "reference_image",
                    "shot7_image",
                    "review_normalize"
                ],
                "on_failure": "shot7_video_fallback"
            },
            {
                "id": "shot7_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot7_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/7_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/7_shot.mp4"
                },
                "label": "镜头7视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 7 video fallback"
            },
            {
                "id": "shot8_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot8_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot8_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/8_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头8图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 8 image",
                "depends_on": [
                    "shot8_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot8_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot8_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot8_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot8_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/8_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/8_shot.png"
                },
                "label": "镜头8视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 8 video",
                "depends_on": [
                    "shot8_vid_prompt",
                    "shot8_duration",
                    "reference_image",
                    "shot8_image",
                    "review_normalize"
                ],
                "on_failure": "shot8_video_fallback"
            },
            {
                "id": "shot8_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot8_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/8_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/8_shot.mp4"
                },
                "label": "镜头8视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 8 video fallback"
            },
            {
                "id": "shot9_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot9_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot9_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/9_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头9图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 9 image",
                "depends_on": [
                    "shot9_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot9_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot9_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot9_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot9_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/9_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/9_shot.png"
                },
                "label": "镜头9视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 9 video",
                "depends_on": [
                    "shot9_vid_prompt",
                    "shot9_duration",
                    "reference_image",
                    "shot9_image",
                    "review_normalize"
                ],
                "on_failure": "shot9_video_fallback"
            },
            {
                "id": "shot9_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot9_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/9_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/9_shot.mp4"
                },
                "label": "镜头9视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 9 video fallback"
            },
            {
                "id": "shot10_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot10_img_prompt",
                "with": {
                    "prompt": "{{ outputs.shot10_img_prompt | truncate(800) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/10_shot.png",
                    "image_size": "1K",
                    "max_retries": 1,
                    "aspect_ratio": "9:16",
                    "fallback_model": "google\/gemini-3-pro-image-preview",
                    "placeholder_on_fail": "yes"
                },
                "label": "镜头10图像",
                "skill": "nano-banana-pro",
                "label_en": "Shot 10 image",
                "depends_on": [
                    "shot10_img_prompt",
                    "review_normalize"
                ]
            },
            {
                "id": "shot10_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize and '__SHOT_ABSENT__' not in outputs.shot10_vid_prompt",
                "with": {
                    "model": "bytedance\/seedance-2.0",
                    "prompt": "Mode: All-Reference. Assets Mapping: reference[1] is the full-cast identity anchor (USE strictly for character likeness, faces, hair, skin tone, outfits, and accessories — keep these byte-identical to the reference across cuts). reference[2] is THIS shot's scene composition reference (USE for camera angle, framing, character blocking, prop placement, and background layout). Shot directive: {{ outputs.shot10_vid_prompt | truncate(700) }}",
                    "duration": "{{ outputs.shot10_duration | int(5) }}",
                    "filename": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/10_shot.mp4",
                    "input_image": "",
                    "max_retries": 2,
                    "aspect_ratio": "9:16",
                    "input_reference": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/reference.png",
                    "input_reference_2": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/10_shot.png"
                },
                "label": "镜头10视频",
                "skill": "seedance-2-prompt",
                "label_en": "Shot 10 video",
                "depends_on": [
                    "shot10_vid_prompt",
                    "shot10_duration",
                    "reference_image",
                    "shot10_image",
                    "review_normalize"
                ],
                "on_failure": "shot10_video_fallback"
            },
            {
                "id": "shot10_video_fallback",
                "kind": "skill_exec",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": "{{ outputs.shot10_duration | int(5) }}",
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/10_shot.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/10_shot.mp4"
                },
                "label": "镜头10视频兜底",
                "skill": "video-still-animator",
                "label_en": "Shot 10 video fallback"
            },
            {
                "id": "ending_image",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "text": "{{ outputs.ending_text_extract | truncate(20) }}",
                    "width": 720,
                    "height": 1280,
                    "output": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/99_ending.png",
                    "subtitle": "",
                    "font_size": 96,
                    "background": "#0a0a10",
                    "text_color": "#e0e0e8"
                },
                "label": "结尾图",
                "skill": "title-card-image",
                "label_en": "Closing image",
                "depends_on": [
                    "ending_text_extract",
                    "review_normalize"
                ]
            },
            {
                "id": "ending_video",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "fps": 24,
                    "width": 720,
                    "height": 1280,
                    "duration": 2,
                    "zoom_rate": 0.0005,
                    "input_image": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/99_ending.png",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/99_ending.mp4"
                },
                "label": "结尾视频",
                "skill": "video-still-animator",
                "label_en": "Closing video",
                "depends_on": [
                    "ending_image",
                    "review_normalize"
                ]
            },
            {
                "id": "merge",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "crf": 22,
                    "fps": 24,
                    "mode": "full",
                    "preset": "medium",
                    "input_dir": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}",
                    "transition": 0.5,
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/final.mp4"
                },
                "label": "视频合并",
                "skill": "video-merger",
                "label_en": "Video merge",
                "depends_on": [
                    "cover_video",
                    "shot1_video",
                    "shot2_video",
                    "shot3_video",
                    "shot4_video",
                    "shot5_video",
                    "shot6_video",
                    "shot7_video",
                    "shot8_video",
                    "shot9_video",
                    "shot10_video",
                    "ending_video",
                    "review_normalize"
                ]
            },
            {
                "id": "subtitles_srt",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "gap_ms": 200,
                    "script": "{{ outputs.final_script }}",
                    "output_path": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/subs.srt",
                    "leading_offset_ms": 2000
                },
                "label": "字幕 SRT",
                "skill": "srt-from-script",
                "label_en": "Subtitle SRT",
                "depends_on": [
                    "final_script",
                    "review_normalize"
                ]
            },
            {
                "id": "subtitled_final",
                "kind": "skill_exec",
                "when": "'DECISION: proceed' in outputs.review_normalize",
                "with": {
                    "input": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/final.mp4",
                    "output": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/final_subtitled.mp4",
                    "margin_v": 80,
                    "font_size": 42,
                    "subtitles": "{{ inputs.workspace_dir }}\/meta_short_drama\/{{ inputs.user_message | slugify | truncate(40) }}\/subs.srt"
                },
                "label": "字幕成片",
                "skill": "subtitle-burner",
                "label_en": "Subtitled video",
                "depends_on": [
                    "merge",
                    "subtitles_srt",
                    "review_normalize"
                ]
            },
            {
                "id": "deliver",
                "kind": "llm_chat",
                "with": {
                    "task": "Compose a 4-10 line summary tailored to the user's decision.\n\nUser original request:\n{{ inputs.user_message | xml_escape | truncate(400) }}\n\nDecision marker:\n{{ outputs.review_normalize | truncate(400) }}\n\nFinal script:\n{{ outputs.final_script | truncate(2500) }}\n\nScript saved at:\n{{ outputs.script_save | truncate(200) }}\n\nMerge output:\n{{ outputs.get('merge', '') | truncate(800) }}\n\nSubtitled-final output:\n{{ outputs.get('subtitled_final', '') | truncate(800) }}\n\nBranching rules:\n- If \"DECISION: proceed\":\n    * Title (from final_script OVERVIEW.TITLE), shot count, total duration.\n    * Headline path = subtitled_final (the burned-in subtitle MP4).\n    * Also list: un-subtitled merge path, SRT path, script.txt path,\n      folder containing intermediates.\n    * Mention HAS_OVERRIDES if yes.\n- If \"DECISION: cancel\":\n    * Acknowledge, note the script was still saved at script_save's\n      path so it's not lost.\n    * Offer to re-trigger.\nRespond in the same language as the user's original request.",
                    "system": "Write a concise delivery message in the user's language. No emoji. Branch on DECISION."
                },
                "label": "交付",
                "label_en": "Delivery",
                "depends_on": [
                    "final_script",
                    "review_normalize",
                    "script_save"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly when the current user asks to generate an AI short-drama or 短剧 from a topic. The workflow infers render style, character identity, and shot count (1-10, default 5) from the request (filling in conservative defaults when missing), drafts a strict shot-by-shot shooting script, pauses for one free-form review (the user can approve, adjust render style \/ character \/ shot count \/ shot details, or cancel in plain language), optionally re-drafts the script with the user's adjustments, generates one universal full-cast identity-reference image plus per-shot composition images, then per-shot video clips (each video anchored to BOTH the universal reference image and its own composition image so the character identity AND scene layout stay consistent), bookends them with a title card and an ending card, burns subtitles in the user's language, and saves the script alongside the final MP4. Do not use it for slide decks, document-decision analysis, single-image generation, isolated script writing, or pasted historical short-drama examples.",
    "policy_tags": [
        "generated-media-review",
        "user-approval-before-media"
    ],
    "eval_prompts": [
        {
            "name": "short-drama-baseline",
            "prompt": "Create a five-shot short-drama plan from a topic, including review status and deliverable locations.",
            "rubric": [
                "Story\/script summary",
                "Review or adjustment status",
                "Generated media status",
                "Saved deliverable locations"
            ]
        }
    ],
    "meta_priority": 75,
    "final_text_mode": "step:deliver",
    "output_contract": {
        "artifacts": [
            {
                "name": "short_drama_video",
                "required": false
            },
            {
                "name": "script_file",
                "required": false
            }
        ],
        "unverified": [
            "Third-party media generation quality until generated assets are inspected."
        ],
        "assumptions": [
            "Visual identity and shot count use conservative defaults when absent."
        ],
        "required_sections": [
            "Story\/script summary",
            "Review or adjustment status",
            "Generated media status",
            "Saved deliverable locations"
        ],
        "append_to_final_text": false
    },
    "preference_keys": [
        "preferred_language",
        "short_drama_render_style"
    ],
    "request_template": {
        "fields": [
            {
                "name": "story_topic",
                "label_en": "Story topic",
                "label_zh": "故事主题",
                "required": true
            },
            {
                "name": "render_style",
                "label_en": "Render style",
                "label_zh": "渲染风格",
                "required": false
            },
            {
                "name": "character_identity",
                "label_en": "Character identity",
                "label_zh": "角色设定",
                "required": false
            },
            {
                "name": "shot_count",
                "default": 5,
                "label_en": "Shot count",
                "label_zh": "镜头数量",
                "required": false
            },
            {
                "name": "audience",
                "default": "short-video viewer",
                "label_en": "Audience",
                "label_zh": "受众",
                "required": false,
                "default_en": "short-video viewer",
                "default_zh": "短视频观众"
            },
            {
                "name": "language",
                "default": "match the user's language",
                "label_en": "Output language",
                "label_zh": "输出语言",
                "required": false,
                "default_en": "match the user's language",
                "default_zh": "跟随用户语言"
            }
        ],
        "outcome": "Short-drama script and generation plan with review pause before media generation.",
        "outcome_en": "Short-drama script and generation plan with review pause before media generation.",
        "outcome_zh": "短剧剧本和生成计划，并在媒体生成前暂停让用户审阅。",
        "assumptions": [
            "Pause for one free-form review before generating media.",
            "Keep shot count between 1 and 10 and use conservative defaults when unspecified."
        ],
        "assumptions_en": [
            "Pause for one free-form review before generating media.",
            "Keep shot count between 1 and 10 and use conservative defaults when unspecified."
        ],
        "assumptions_zh": [
            "生成媒体前会暂停一次，允许用户自由审阅和修改。",
            "镜头数量保持在 1 到 10 之间，未说明时使用保守默认值。"
        ]
    }
}

{
    "kind": "meta",
    "name": "meta-skill-creator",
    "always": false,
    "triggers": [
        "新增 meta 技能",
        "组合现有 skill 成 meta-skill",
        "create a meta-skill",
        "new meta-skill",
        "orchestrates existing skills",
        "orchestrates search",
        "compose existing skills",
        "synthesize meta-skill",
        "compose meta-skill"
    ],
    "provenance": {
        "origin": "opensquilla-original",
        "license": "Apache-2.0"
    },
    "composition": {
        "steps": [
            {
                "id": "clarify_intent",
                "kind": "llm_chat",
                "with": {
                    "task": "Clarify whether the user wants a meta-skill, not a normal standalone\nskill. If the request is generic skill creation, return\nROUTE: normal-skill. If it requires orchestrating multiple existing\nskills, return ROUTE: meta-skill. Also summarize desired inputs,\noutputs, trigger phrases, and whether a human preference branch is\nneeded. Set NEEDS_CLARIFICATION: yes only when the workflow goal,\noutput shape, trigger boundary, or human preference branch is\ngenuinely ambiguous and the request is an interactive user request.\nFor unattended auto-propose, dream, or cron activation, set\nNEEDS_CLARIFICATION: no and continue from available context.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nOuter system \/ activation context:\n{{ inputs.system_prompt | default(\"\") | xml_escape | truncate(1200) }}\n\nReturn:\nROUTE: <normal-skill|meta-skill>\nWORKFLOW_GOAL: <goal or unclear>\nOUTPUT_SHAPE: <deliverable or unclear>\nTRIGGERS: <phrases or unclear>\nHUMAN_PREFERENCE_BRANCH: <yes|no|unclear>\nNEEDS_CLARIFICATION: <yes|no>\nMISSING_FIELDS:\n  - <workflow_goal|output_shape|trigger_boundary|human_preference_branch|none>\nCLARIFY_REASON: <one concise reason, or none>\n",
                    "system": "You are the intent gate for meta-skill-creator. Do not inspect\nworkspace files, history, memory, or external sources. Decide only\nfrom the explicit user request and activation context.\n"
                },
                "label": "意图澄清",
                "label_en": "Intent clarification"
            },
            {
                "id": "creator_clarify",
                "kind": "user_input",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and 'needs_clarification: yes' in (outputs.clarify_intent | lower)",
                "label": "创建澄清",
                "clarify": {
                    "mode": "form",
                    "intro": "新 meta-skill 的边界还不够明确。请补齐目标和输出形态，避免生成过宽的触发词。\n",
                    "fields": [
                        {
                            "name": "workflow_goal",
                            "type": "string",
                            "prompt": "工作流目标 \/ Workflow goal",
                            "required": true,
                            "max_chars": 300,
                            "prompt_en": "Workflow goal",
                            "prompt_zh": "工作流目标"
                        },
                        {
                            "name": "output_shape",
                            "type": "string",
                            "prompt": "最终输出形态 \/ Output shape",
                            "required": true,
                            "max_chars": 200,
                            "prompt_en": "Output shape",
                            "prompt_zh": "最终输出形态"
                        },
                        {
                            "name": "trigger_boundary",
                            "type": "string",
                            "prompt": "触发边界或不要覆盖的场景 \/ Trigger boundary",
                            "max_chars": 300,
                            "prompt_en": "Trigger boundary or cases to avoid",
                            "prompt_zh": "触发边界或不要覆盖的场景"
                        },
                        {
                            "name": "human_preference_branch",
                            "type": "bool",
                            "prompt": "是否需要运行中让用户选择偏好 \/ Need human preference branch?",
                            "default": false,
                            "prompt_en": "Need a human preference branch during the run?",
                            "prompt_zh": "是否需要运行中让用户选择偏好"
                        }
                    ],
                    "intro_en": "The new meta-skill boundary is not clear enough. Fill in the goal and output shape so the trigger stays precise.",
                    "intro_zh": "新 meta-skill 的边界还不够明确。请补齐目标和输出形态，避免生成过宽的触发词。",
                    "nl_extract": true,
                    "timeout_hours": 24,
                    "cancel_keywords": [
                        "算了",
                        "取消",
                        "cancel",
                        "stop",
                        "abort"
                    ]
                },
                "label_en": "Creation clarification",
                "depends_on": [
                    "clarify_intent"
                ]
            },
            {
                "id": "normal_skill_exit",
                "kind": "tool_call",
                "tool": "emit_text",
                "when": "'route: normal-skill' in (outputs.clarify_intent | lower)",
                "label": "普通技能退出",
                "label_en": "Regular skill exit",
                "tool_args": {
                    "text": "This request was classified as a normal standalone skill request, not\na meta-skill composition request. The meta-skill creator stopped\nbefore proposal assembly or persistence.\n"
                },
                "depends_on": [
                    "clarify_intent"
                ]
            },
            {
                "id": "creator_mode",
                "kind": "llm_classify",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "with": {
                    "text": "Classify how far the creator workflow should go.\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nOuter system \/ activation context:\n{{ inputs.system_prompt | default(\"\") | xml_escape | truncate(1200) }}\n\nClarified intent:\n{{ outputs.clarify_intent | truncate(1200) }}\n\nClarification answers (may be empty when not needed):\n{{ inputs.get('collected', {}).get('creator_clarify', {}) | tojson }}\n\nDecision rules:\n- PREVIEW_ONLY: user asks for an example, template, plan, draft,\n  or wants to inspect before writing\/persisting anything.\n- PERSISTED_PROPOSAL: user asks to create\/save\/write\/propose a\n  meta-skill but does not ask for exhaustive smoke testing.\n- FULL_GATED: user asks for a production-ready, accepted, tested,\n  validated, or fully gated meta-skill.\n- FULL_GATED: unattended auto-propose, dream, or cron activation\n  requires preserving all creator gates before any auto-enable\n  decision.\n"
                },
                "label": "创建模式",
                "label_en": "Creation mode",
                "depends_on": [
                    "clarify_intent",
                    "creator_clarify"
                ],
                "output_choices": [
                    "PREVIEW_ONLY",
                    "PERSISTED_PROPOSAL",
                    "FULL_GATED"
                ]
            },
            {
                "id": "harvest",
                "kind": "skill_exec",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and 'Unattended meta-skill auto-propose run' in inputs.get('system_prompt', '')",
                "with": {
                    "query": "Co-occurring skill chains and meta-skill usage for: {{ outputs.clarify_intent | truncate(1000) }}\nClarification answers:\n{{ inputs.get('collected', {}).get('creator_clarify', {}) | tojson }}\n",
                    "include": [
                        "co_occurrences",
                        "meta_usage",
                        "router_fixtures"
                    ],
                    "window_days": 30
                },
                "label": "需求采集",
                "skill": "history-explorer",
                "label_en": "Requirement capture",
                "depends_on": [
                    "clarify_intent",
                    "creator_clarify"
                ],
                "on_failure": "harvest_empty"
            },
            {
                "id": "harvest_empty",
                "kind": "tool_call",
                "tool": "emit_text",
                "label": "空采集兜底",
                "label_en": "Empty-capture fallback",
                "tool_args": {
                    "text": "no history available; downstream should rely on user intent only"
                }
            },
            {
                "id": "pick_pattern",
                "kind": "llm_classify",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "with": {
                    "user_intent": "Raw user request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nClarified intent:\n{{ outputs.clarify_intent | truncate(1000) }}\n",
                    "history_summary": "{{ outputs.harvest | truncate(2000) }}"
                },
                "label": "模式选择",
                "label_en": "Mode selection",
                "depends_on": [
                    "creator_mode",
                    "harvest"
                ],
                "output_choices": [
                    "p1_sequential",
                    "p2_fan_out_merge",
                    "p3_condition_gated"
                ]
            },
            {
                "id": "fill_slots",
                "kind": "tool_call",
                "tool": "meta_skill_fill_slots",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "label": "填充槽位",
                "label_en": "Fill slots",
                "tool_args": {
                    "pattern_id": "{{ outputs.pick_pattern }}",
                    "user_intent": "Raw user request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nClarified intent:\n{{ outputs.clarify_intent | truncate(1000) }}\n",
                    "history_summary": "{{ outputs.harvest | truncate(2000) }}"
                },
                "depends_on": [
                    "pick_pattern"
                ]
            },
            {
                "id": "assemble",
                "kind": "tool_call",
                "tool": "meta_skill_assemble",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "label": "组装",
                "label_en": "Assembly",
                "tool_args": {
                    "pattern_id": "{{ outputs.pick_pattern }}",
                    "slots_json": "{{ outputs.fill_slots }}"
                },
                "depends_on": [
                    "fill_slots"
                ]
            },
            {
                "id": "collision_check",
                "kind": "llm_chat",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "with": {
                    "task": "Review this generated meta-skill proposal for trigger collisions with\nexisting bundled skills. Flag generic triggers, overlaps with\nmeta-skill-creator, and broad phrases that would steal unrelated user\nintent. Return PASS or REVISE_NEEDED plus reasons.\n\nCandidate SKILL.md:\n{{ outputs.assemble | truncate(8000) }}\n",
                    "system": "You are a trigger-collision reviewer for meta-skill-creator. Use only\nthe candidate SKILL.md provided in the task and the bundled creator\nboundaries named there. Do not call tools or inspect the workspace.\n"
                },
                "label": "冲突检查",
                "label_en": "Conflict check",
                "depends_on": [
                    "assemble"
                ]
            },
            {
                "id": "lint",
                "kind": "tool_call",
                "tool": "meta_skill_lint_run",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "label": "Lint 检查",
                "label_en": "Lint check",
                "tool_args": {
                    "gates": "G1,G2",
                    "skill_md": "{{ outputs.assemble }}"
                },
                "depends_on": [
                    "collision_check"
                ]
            },
            {
                "id": "risk_classify",
                "kind": "llm_chat",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "with": {
                    "task": "Classify operational risk for the generated meta-skill. Consider file\nwrites, network access, GitHub\/gh actions, shell commands, memory\nwrites, and destructive operations. Return:\nRISK: low|medium|high\nCAPABILITIES:\n  - <capability>\nREQUIRED_GATES:\n  - <gate>\n\nCandidate SKILL.md:\n{{ outputs.assemble | truncate(8000) }}\n\nLint result:\n{{ outputs.lint | truncate(2000) }}\n",
                    "system": "You are an operational-risk classifier for generated meta-skills. Use\nonly the candidate SKILL.md and lint result in the task. Do not call\ntools or inspect the workspace.\n"
                },
                "label": "风险分类",
                "label_en": "Risk classification",
                "depends_on": [
                    "lint"
                ]
            },
            {
                "id": "single_model_baseline",
                "kind": "llm_chat",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and outputs.creator_mode == 'FULL_GATED'",
                "with": {
                    "task": "Same task as the orchestrated meta-skill creator workflow, but solve\nit as a standalone highest-tier model response. Use the outer system\nprompt and raw user request below; do not rely on any meta-skill\nintermediate output.\n\nOuter system prompt:\n{{ inputs.system_prompt | xml_escape | truncate(12000) }}\n\nUser request:\n{{ inputs.user_message | xml_escape | truncate(1600) }}\n\nReturn:\n- proposed meta-skill name\n- triggers\n- inputs\n- step graph\n- gates\n- collision risks\n- SKILL.md preview\n\nBoundary rule:\nCreator validation, proposal persistence, auto-enable decisions,\nand gate execution are handled by the outer meta-skill-creator\nworkflow. Do not require the generated candidate SKILL.md itself to\ncontain steps for saving proposals, running creator gates, comparing\nagainst baselines, or deciding auto-enable. The candidate SKILL.md\nshould describe only the reusable business workflow that will run\nlater when the new meta-skill is invoked.\n",
                    "system": "You are the highest-tier baseline model for meta-skill authoring.\nSolve the same task directly in one pass under the same outer\nassistant system prompt and user request, but without history\nmining, intent clarification output, deterministic slot filling,\nlint tools, smoke tools, persistence, or sub-skill orchestration.\nProduce the strongest standalone SKILL.md candidate you can from\nthat full prompt context.\n"
                },
                "label": "单模基线",
                "label_en": "Single-mode baseline",
                "depends_on": [
                    "creator_mode"
                ]
            },
            {
                "id": "acceptance_compare",
                "kind": "llm_chat",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and outputs.creator_mode == 'FULL_GATED'",
                "with": {
                    "task": "User request:\n{{ inputs.user_message | xml_escape | truncate(1200) }}\n\nOrchestrated candidate:\n{{ outputs.assemble | truncate(7000) }}\n\nSingle-model baseline:\n{{ outputs.single_model_baseline | truncate(7000) }}\n\nReturn this exact structure:\nWINNER: orchestrated|single-model|tie\nQUALITY_SCORE: <0.00-1.00 weighted final product quality score>\nREASONS:\n- <specific evidence>\nREGRESSIONS:\n- <what the orchestrated candidate lacks versus the baseline>\nREQUIRED_IMPROVEMENTS:\n- <blocking edit required before acceptance, or \"none\">\n\nTreat REQUIRED_IMPROVEMENTS as a hard acceptance gate. Do not list\noptional nice-to-have enhancements there. If the orchestrated\ncandidate is production-acceptable and any baseline advantages are\nnon-blocking, put those advantages under REGRESSIONS and set\nREQUIRED_IMPROVEMENTS to \"none\".\nScore final product quality with high weight: 40% usefulness and\ncompleteness of the generated SKILL.md, 25% trigger\/input\/output\nspecificity, 20% gate\/risk\/collision coverage, and 15% reusable\nworkflow generality. Scores below 0.80 are not acceptable for\nFULL_GATED persistence even when WINNER is orchestrated.\nNever make proposal persistence, auto-enable state, acceptance\ncomparison, or runtime E2E execution a REQUIRED_IMPROVEMENT for the\ncandidate SKILL.md; those are already performed by this outer creator\nworkflow and are evaluated from the creator's gate outputs.\n",
                    "system": "You are an acceptance reviewer. Compare an orchestrated candidate\nagainst a single-model baseline that used the highest-tier model on\nthe same task. Reward verifiable skill composition, trigger safety,\ngates, operational risk handling, and reusable SKILL.md quality.\nKeep the boundary strict: proposal persistence, gate execution,\nruntime E2E, acceptance comparison, and auto-enable decisions belong\nto the outer meta-skill-creator workflow. Do not penalize a candidate\nSKILL.md for omitting creator-workflow steps that should not run when\nthe generated meta-skill is invoked later.\n"
                },
                "label": "验收对比",
                "label_en": "Acceptance comparison",
                "depends_on": [
                    "assemble",
                    "single_model_baseline"
                ]
            },
            {
                "id": "smoke",
                "kind": "tool_call",
                "tool": "meta_skill_smoke_run",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and outputs.creator_mode != 'PREVIEW_ONLY'",
                "label": "冒烟测试",
                "label_en": "Smoke test",
                "tool_args": {
                    "skill_md": "{{ outputs.assemble }}",
                    "classifier_model": "openrouter\/auto",
                    "fixture_gen_model": "openai\/gpt-4o-mini"
                },
                "depends_on": [
                    "risk_classify"
                ]
            },
            {
                "id": "runtime_e2e",
                "kind": "tool_call",
                "tool": "meta_skill_runtime_e2e_run",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and outputs.creator_mode == 'FULL_GATED'",
                "label": "运行时 E2E",
                "label_en": "Runtime E2E",
                "tool_args": {
                    "skill_md": "{{ outputs.assemble }}",
                    "eval_prompts": ""
                },
                "depends_on": [
                    "assemble",
                    "smoke"
                ]
            },
            {
                "id": "preview",
                "kind": "llm_chat",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower)",
                "with": {
                    "task": "Produce a concise proposal preview for the user\/operator before\npersistence. Include proposed name, triggers, DAG summary, collision\nresult, risk classification, lint status, smoke status, baseline\ncomparison status, and whether it appears eligible for acceptance.\nDo not invent paths or proposal IDs.\n\nCandidate SKILL.md:\n{{ outputs.assemble | truncate(8000) }}\n\nCollision check:\n{{ outputs.collision_check | truncate(1200) }}\n\nRisk:\n{{ outputs.risk_classify | truncate(1200) }}\n\nCreator mode:\n{{ outputs.creator_mode }}\n\nLint:\n{{ outputs.lint | truncate(2000) }}\n\nSmoke:\n{{ outputs.smoke | truncate(2000) }}\n\nBaseline comparison:\n{{ outputs.acceptance_compare | truncate(2000) }}\n\nRuntime E2E:\n{{ outputs.runtime_e2e | truncate(2000) }}\n",
                    "system": "You are the final preview writer for meta-skill-creator. Produce only\na concise operator-facing proposal preview from the supplied step\noutputs. Do not call tools, inspect files, or invent persistence IDs.\n"
                },
                "label": "预览",
                "label_en": "Preview",
                "depends_on": [
                    "smoke",
                    "acceptance_compare",
                    "runtime_e2e"
                ]
            },
            {
                "id": "persist",
                "kind": "tool_call",
                "tool": "meta_skill_persist_proposal",
                "when": "'route: meta-skill' in (outputs.clarify_intent | lower) and outputs.creator_mode != 'PREVIEW_ONLY'",
                "label": "保存",
                "label_en": "Save",
                "tool_args": {
                    "skill_md": "{{ outputs.assemble }}",
                    "lint_result": "{{ outputs.lint }}",
                    "risk_result": "{{ outputs.risk_classify }}",
                    "creator_mode": "{{ outputs.creator_mode }}",
                    "smoke_result": "{{ outputs.smoke }}",
                    "collision_result": "{{ outputs.collision_check }}",
                    "acceptance_result": "{{ outputs.acceptance_compare }}",
                    "runtime_e2e_result": "{{ outputs.runtime_e2e }}"
                },
                "depends_on": [
                    "preview"
                ]
            },
            {
                "id": "final_response",
                "kind": "tool_call",
                "tool": "emit_text",
                "label": "最终回复",
                "label_en": "Final response",
                "tool_args": {
                    "text": "{% if outputs.normal_skill_exit %}\n{{ outputs.normal_skill_exit }}\n{% else %}\n{{ outputs.preview }}\n{% endif %}"
                },
                "depends_on": [
                    "preview",
                    "normal_skill_exit"
                ]
            }
        ]
    },
    "description": "Use this meta-skill instead of answering directly only when the current user explicitly asks to create, compose, synthesize, or propose a new meta-skill that orchestrates multiple existing skills. It uses multi-skill orchestration for intent clarification, optional history mining, trigger-collision checks, linting, smoke\/runtime gates, preview, and optional proposal persistence. Do not use it for creating a normal standalone skill, asking how meta-skills work, analyzing pasted skill lists, or discussing existing meta-skills.",
    "policy_tags": [
        "trigger-collision-check",
        "lint-before-enable"
    ],
    "eval_prompts": [
        {
            "name": "meta-skill-creator-baseline",
            "prompt": "Draft a meta-skill that orchestrates search, synthesis, validation, and proposal persistence.",
            "rubric": [
                "Intent summary",
                "Proposed DAG",
                "Trigger and collision notes",
                "Validation results",
                "Save or next-step status"
            ]
        }
    ],
    "meta_priority": 90,
    "final_text_mode": "step:final_response",
    "output_contract": {
        "artifacts": [
            {
                "name": "meta_skill_proposal",
                "required": false
            }
        ],
        "unverified": [
            "Live runtime smoke results when execution gates are unavailable."
        ],
        "assumptions": [
            "Preview mode is used unless persistence is explicit."
        ],
        "required_sections": [
            "Intent summary",
            "Proposed DAG",
            "Trigger and collision notes",
            "Validation results",
            "Save or next-step status"
        ],
        "append_to_final_text": false
    },
    "preference_keys": [
        "preferred_language",
        "meta_authoring_style"
    ],
    "request_template": {
        "fields": [
            {
                "name": "meta_skill_goal",
                "label_en": "Meta-skill goal",
                "label_zh": "Meta-skill 目标",
                "required": true
            },
            {
                "name": "existing_skills_to_orchestrate",
                "label_en": "Existing skills to orchestrate",
                "label_zh": "要编排的现有技能",
                "required": false
            },
            {
                "name": "save_or_preview",
                "default": "preview unless the user asks to persist",
                "label_en": "Save or preview",
                "label_zh": "保存或预览",
                "required": false,
                "default_en": "preview unless the user asks to persist",
                "default_zh": "默认预览；仅在用户要求持久化时保存"
            },
            {
                "name": "constraints",
                "label_en": "Constraints",
                "label_zh": "限制条件",
                "required": false
            },
            {
                "name": "audience",
                "default": "meta-skill author",
                "label_en": "Audience",
                "label_zh": "受众",
                "required": false,
                "default_en": "meta-skill author",
                "default_zh": "meta-skill 作者"
            },
            {
                "name": "language",
                "default": "match the user's language",
                "label_en": "Output language",
                "label_zh": "输出语言",
                "required": false,
                "default_en": "match the user's language",
                "default_zh": "跟随用户语言"
            }
        ],
        "outcome": "Proposed meta-skill spec or saved proposal with trigger, DAG, tests, and validation notes.",
        "outcome_en": "Proposed meta-skill spec or saved proposal with trigger, DAG, tests, and validation notes.",
        "outcome_zh": "生成 meta-skill 提案或保存方案，包含触发词、DAG、测试和验证记录。",
        "assumptions": [
            "Create a meta-skill only when orchestration is explicitly requested.",
            "Check trigger collisions and lint before presenting a proposal."
        ],
        "assumptions_en": [
            "Create a meta-skill only when orchestration is explicitly requested.",
            "Check trigger collisions and lint before presenting a proposal."
        ],
        "assumptions_zh": [
            "仅在用户明确要求编排时创建 meta-skill。",
            "展示提案前检查触发词冲突并运行 lint。"
        ]
    }
}

{
    "name": "multi-search-engine",
    "homepage": "",
    "metadata": {
        "platform": {
            "emoji": "🔍",
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "entrypoint": {
        "args": [
            "--query",
            "{{ with.query | default(inputs.user_message) }}",
            "--engines",
            "{{ with.engines | default(['brave', 'duckduckgo']) | join(',') }}",
            "--limit",
            "{{ with.max_results | default(25) }}",
            "--json"
        ],
        "parse": "json",
        "command": "python {baseDir}\/scripts\/search.py",
        "timeout": 60
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/multi-search-engine",
        "maintained_by": "OpenSquilla"
    },
    "description": "Query the web through multiple search engines (Brave, Tavily, SerpAPI, DuckDuckGo, Bing, Baidu, Sogou, 360) with a single CLI surface. Trigger when the user asks for a research search, fact lookup, source discovery, or wants to compare engines for coverage. The skill aggregates per-engine result lists and normalizes them into a uniform JSON shape for downstream skills (deep-research is the primary consumer). API-key engines gate themselves on the relevant environment variable; engines requiring no key always run."
}

{
    "name": "pdf-toolkit",
    "homepage": "https:\/\/pypdf.readthedocs.io\/",
    "metadata": {
        "platform": {
            "emoji": "📕",
            "install": [
                {
                    "id": "pypdf",
                    "kind": "uv",
                    "label": "Install pypdf (uv pip)",
                    "package": "pypdf"
                },
                {
                    "id": "reportlab",
                    "kind": "uv",
                    "label": "Install reportlab (uv pip)",
                    "package": "reportlab"
                }
            ],
            "requires": {
                "anyBins": [
                    "python",
                    "python3"
                ]
            }
        }
    },
    "provenance": {
        "origin": "clawhub-mit0",
        "license": "MIT-0",
        "upstream_url": "https:\/\/clawhub.ai\/pdf",
        "maintained_by": "OpenSquilla"
    },
    "description": "Structured `.pdf` operations: extract text\/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract pages 5-12, fill a tax form, or build a new PDF from data. Distinct from `nano-pdf`, which uses an LLM to rewrite a page from a sentence; this skill is deterministic byte-level work via pypdf, pdfplumber, and reportlab."
}

{
    "name": "skill-creator",
    "provenance": {
        "origin": "openclaw-derived",
        "license": "MIT",
        "upstream_url": "https:\/\/github.com\/openclaw\/openclaw",
        "maintained_by": "OpenSquilla"
    },
    "description": "Create, edit, improve, or audit AgentSkills. Use when creating a new skill from scratch or when asked to improve, review, audit, tidy up, or clean up an existing skill or SKILL.md file. Also use when editing or restructuring a skill directory (moving files to references\/ or scripts\/, removing stale content, validating against the AgentSkills spec). Triggers on phrases like \"create a skill\", \"author a skill\", \"tidy up a skill\", \"improve this skill\", \"review the skill\", \"clean up the skill\", \"audit the skill\"."
}

{
    "name": "sub-agent",
    "metadata": {
        "openclaw": {
            "emoji": "🧩",
            "install": [
                {
                    "id": "node-claude",
                    "bins": [
                        "claude"
                    ],
                    "kind": "node",
                    "label": "Install Claude Code CLI (npm)",
                    "package": "@anthropic-ai\/claude-code"
                },
                {
                    "id": "node-codex",
                    "bins": [
                        "codex"
                    ],
                    "kind": "node",
                    "label": "Install Codex CLI (npm)",
                    "package": "@openai\/codex"
                }
            ],
            "requires": {
                "anyBins": [
                    "claude",
                    "codex",
                    "opencode",
                    "pi"
                ]
            }
        },
        "opensquilla": {
            "requires_tools": [
                "background_process",
                "exec_command",
                "process"
            ]
        }
    },
    "provenance": {
        "origin": "openclaw-derived",
        "license": "MIT",
        "upstream_url": "https:\/\/github.com\/openclaw\/openclaw",
        "maintained_by": "OpenSquilla"
    },
    "description": "Delegate a self-contained task to a sub-Agent (Codex, Claude Code, or Pi via background process). The original use case was coding tasks — building features, reviewing PRs, refactoring — but the skill is the generic \"spawn a sub-Agent with full tool surface\" slot used by meta-skill DAG steps for any LLM-driven sub-task (policy review, trace parsing, report synthesis, document generation). Renamed from ``coding-agent`` to reflect actual usage; the wrapped CLIs (codex \/ claude \/ pi) still bias toward coding workloads. Use when: (1) building\/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative tasks that need file exploration, (5) meta-skill steps requiring full tool\/LLM agency. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn\/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:\"acp\"), or any work in ~\/clawd workspace (never spawn agents here). Prefer non-interactive CLI modes such as codex exec, claude --print, opencode run, or pi -p."
}