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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。

Not recommendedcritical or high security findings
ClawHub Agent Skills author: miemie_le v1.0.0 MIT-0 21 files · 10 scripts body ≈ 1 836 tokens Open the sourceclawhub.ai↗ analyzed 6 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
C
81/100
safety, quality, tests
Safety 60%
77
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 2

  • high Dangerous commands cmd-pipe-to-shell docs/troubleshooting.md:35
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL https://get.docker.com | sh
Medium and low: 1
  • medium Dangerous commands cmd-pipe-to-shell docs/cold_start.md:102
    Downloads and executes remote code from an unrecognised host (pipe to shell) (quoted — discussed, not commanded)
    > - Linux：请执行 `curl -fsSL https://get.docker.com | sh`
    quoted

Files scanned: 20. 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