scenario-lab

Agent Skills › YSLAB-ai/scenario-lab › scenario-lab

scenario-lab

GitHub

指导在Claude Code中通过CLI高效使用Scenario Lab。强调利用run-adapter-action驱动工作流，避免加载大文件至上下文。遵循turn反馈确定下一步，规范证据摄入与报告生成流程，支持结构化输入及手动恢复命令。

adapters/claude/skills/scenario-lab/SKILL.md YSLAB-ai/scenario-lab

触发场景

需要运行Scenario Lab工作流处理场景实验室的证据数据生成或审查场景报告

安装

npx skills add YSLAB-ai/scenario-lab --skill scenario-lab -g -y

更多选项

非标准路径

npx skills add https://github.com/YSLAB-ai/scenario-lab/tree/main/adapters/claude/skills/scenario-lab -g -y

不安装直接使用

npx skills use YSLAB-ai/scenario-lab@scenario-lab

指定 Agent (Claude Code)

npx skills add YSLAB-ai/scenario-lab --skill scenario-lab -a claude-code -g -y

安装 repo 全部 skill

npx skills add YSLAB-ai/scenario-lab --all -g -y

预览 repo 内 skill

npx skills add YSLAB-ai/scenario-lab --list

SKILL.md

Frontmatter

{
    "name": "scenario-lab",
    "description": "Use the local Scenario Lab CLI from Claude Code without loading full run artifacts into active context."
}

From the checked-out Scenario Lab repo/workspace, create or activate a Python 3.12+ virtualenv and install the shared core package with pip install -e 'packages/core[dev]'.
Use the query-style commands to drive the workflow instead of loading full run artifacts into active context.
Prefer direct structured input over temporary JSON files for the normal adapter path.
The underlying raw commands still exist, including scenario-lab start-run, scenario-lab save-intake-draft, and scenario-lab draft-conversation-turn, but the packaged runtime is the normal path.
Bootstrap a workflow slice with scenario-lab run-adapter-action --root <root> --run-id <run> --revision-id r1 --action start-run --domain-pack <slug>.
after each workflow mutation, keep using scenario-lab run-adapter-action --root <root> [--candidate-path <dir>] --run-id <run> --revision-id <rev> --action <action-name> ... and treat the returned turn as the user-facing next step. The default evidence corpus is <root>/corpus.db; only pass --corpus-db <db> when intentionally using a separate evidence database.
Use turn.recommended_runtime_action as the default next runtime action and turn.actions as the ordered set of allowed next steps. This keeps the conversation loop deterministic without manually sequencing raw workflow commands.
Do not manually sequence scenario-lab draft-intake-guidance, scenario-lab draft-retrieval-plan, or scenario-lab draft-ingestion-plan in the normal path. Consume those payloads only through the packaged runtime turn.context.
When the evidence-stage runtime context includes ingestion_recommendations, prefer --action batch-ingest-recommended before --action draft-evidence-packet. If no corpus exists yet, first save gathered evidence files under <root>/evidence-candidates/ and pass that directory as --candidate-path. Then trim the packet in place with --action curate-evidence-draft.
After approval, keep using the packaged runtime to reach simulation, report review, and begin-revision-update. Prefer scenario-lab summarize-revision / scenario-lab summarize-run before opening full report files.
Raw commands such as scenario-lab draft-evidence-packet, scenario-lab curate-evidence-draft, scenario-lab draft-approval-packet, scenario-lab approve-revision, scenario-lab begin-revision-update, scenario-lab simulate, scenario-lab summarize-run, scenario-lab summarize-revision, scenario-lab save-evidence-draft, and scenario-lab generate-report remain available for inspection or manual recovery outside the packaged runtime path. save-evidence-draft now also accepts repeated --item-json payloads for direct structured evidence replacement when a file handoff is unnecessary.