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AC jy-knowledge-skill

智能知识库文件处理与数据集自动生成系统。上传文件后自动评估知识价值，LLM智能分类归档，通过EasyDataset生成微调数据集。使用场景：(1) 用户上传文档需要判断是否值得生成知识数据集，(2) 批量处理DOCX/PDF/Excel/图片文件转为知识库，(3) 从本地知识库搜索答案结合对话回复，(4) 部署和初始化知识库环境，(5) 管理知识库分类体系和数据集。

ClawHub Agent Skills author: miemie_le v1.0.0 MIT-0 21 files · 1 script body ≈ 1 836 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

智能知识库文件处理与数据集自动生成系统。上传文件后自动评估知识价值，LLM智能分类归档，通过EasyDataset生成微调数据集。使用场景：(1) 用户上传文档需要判断是否值得生成知识数据集，(2) 批量处理DOCX/PDF/Excel/图片文件转为知识库，(3) 从本地知识库搜索答案结合对话回复，(4)…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

ProcedureWordDockerExcelPDFData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (jy-knowledge-skill) differs from the folder (jy-knowlgdge-skill)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 20 steps
    • 100Execution cost. Instruction body is 1836 tokens
    • 100Running it twice. No mutating operations
    • low 12 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +2Single-language instructions
    • +3Description length 184: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (19 code blocks)
    • +4Reference files are cited in the instructions (5 of 6)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill appears purpose-built for knowledge-base generation, but it needs review because it processes private documents through LLM services and deploys unauthenticated local services that can store or expose that data.
    LLM: suspicious (high) · 30 Jul 2026