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审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查，并输出详细的问题报告和改进建议。

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 5 files body ≈ 2 207 tokens Open the sourcegithub.com↗ analyzed 2 d ago

审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML…

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerFigmaAI 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
D
41/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: skill-reviewer (LeoYeAI/openclaw-master-skills)

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: 5. 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")

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (skill-reviewer) differs from the folder (skill-reviewer-2)
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 100Steps. 45 steps
  • 100Execution cost. Instruction body is 2207 tokens
  • 100Running it twice. No mutating operations

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 173: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 45 items
  • +4Has examples (28 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.