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检查你安装的所有 AI Skill：列出完整清单、找出内容重复或互相冲突的 Skill、评估每个是否成熟可靠，并告诉你怎么处理（放心用/合并/删除）。当用户问"我有哪些 skill / 哪些重复了 / skill 有没有问题 / 该留哪个"时调用。创建新 Skill、单体质量自检、C 盘清理、代码审查或安全扫描、纯知识问答时不要调用。

ClawHub Agent Skills author: songzhou666 v0.1.2 MIT-0 35 files body ≈ 1 218 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

检查你安装的所有 AI Skill：列出完整清单、找出内容重复或互相冲突的 Skill、评估每个是否成熟可靠，并告诉你怎么处理（放心用/合并/删除）。当用户问"我有哪些 skill / 哪些重复了 / skill 有没有问题 / 该留哪个"时调用。创建新 Skill、单体质量自检、C…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureCustomer supporttype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 34. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "language"

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 41 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1218 tokens
  • 100Running it twice. No mutating operations
  • low 13 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 168: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: clean
This is a disclosed local skill-auditing tool that scans installed skills and writes its own .medic reports, with no evidence of hidden exfiltration or unsafe mutation.
LLM: benign (high) · VirusTotal: · 8 Aug 2026