Agent Skillszerogpu/zerogpu-router › classify-structured

classify-structured

GitHub

通过JSON Schema对文本进行多轴分类,如情感与主题分析。调用ZeroGPU API执行,需使用heredoc传递原文及标签定义。

agents/openclaw/plugin/skills/classify-structured/SKILL.md zerogpu/zerogpu-router

Trigger Scenarios

需要对文本进行多维度分类 指定了具体的分类维度及对应标签集合

Install

npx skills add zerogpu/zerogpu-router --skill classify-structured -g -y
More Options

Non-standard path

npx skills add https://github.com/zerogpu/zerogpu-router/tree/main/agents/openclaw/plugin/skills/classify-structured -g -y

Use without installing

npx skills use zerogpu/zerogpu-router@classify-structured

指定 Agent (Claude Code)

npx skills add zerogpu/zerogpu-router --skill classify-structured -a claude-code -g -y

安装 repo 全部 skill

npx skills add zerogpu/zerogpu-router --all -g -y

预览 repo 内 skill

npx skills add zerogpu/zerogpu-router --list

SKILL.md

Frontmatter
{
    "name": "classify-structured",
    "metadata": {
        "openclaw": {
            "install": [
                {
                    "bins": [
                        "zerogpu"
                    ],
                    "kind": "node",
                    "package": "zerogpu-cli"
                }
            ],
            "requires": {
                "bins": [
                    "zerogpu"
                ]
            }
        }
    },
    "description": "Multi-axis classification using a JSON schema mapping categories to allowed labels (gliner2-base-v1). Use when the user wants to classify text along several dimensions at once, e.g. \"classify by sentiment and topic\" with explicit label sets per axis.",
    "allowed-tools": "Bash(zerogpu classify_structured*)",
    "argument-hint": "<text> -s '<json schema>'"
}

Sends your input to ZeroGPU's hosted API for inference — this is not local processing. Don't pass secrets, credentials, or regulated data you aren't cleared to share with a third party. See the plugin README's "Data & privacy" section.

Run schema-driven classification:

zerogpu classify_structured $ARGUMENTS

Quoting (required, to survive shell parsing of arbitrary user text): format $ARGUMENTS with the source text wrapped via heredoc command substitution, then flags after. Inside the heredoc, paste the user's text verbatim — do not escape:

"$(cat <<'ZGPU_T'
<the source text, verbatim, multi-line and special chars all OK>
ZGPU_T
)" -s '{"sentiment":["positive","negative","neutral"],"topic":["support","billing","product"]}'

Schema is a single-quoted JSON object mapping each axis to its allowed labels. Output is a JSON object with one chosen label per category.

Savings note: only if the command output literally contains a line starting with 💰 ZeroGPU savings, append that exact line, unchanged, as the last line of your reply. If no such line is present, say nothing about savings and do not mention or suggest the cost-savings skill — this note is intentionally occasional, not shown every time.

Version History

  • cee9321 Current 2026-08-02 21:15

    更新依赖库并修复安全问题

  • 7b9b503 2026-07-24 11:48

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Metadata

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Version
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2026-07-24 11:48

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