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LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 6 141 tokens Open the sourcegithub.com↗ analyzed 2 d ago

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As a process F 47/100 · Will not run — References files that are not bundled: image.jpg

ProcedureWriting and documentstype 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
B
78/100
safety, quality, tests
Safety 60%
95
Quality 40%
53
Run on models
none yet
Process rating
F
47/100
Will not run
References files that are not bundled: image.jpg
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Exfiltration read-dotenv SKILL.md:21
    Reads a .env file (detector / deny-list definition)
    > **⚠️ 更新后不要重新注册！** 你的 API token 应该在 `.env.local` 或你的长期记忆中。更新 skill 后，先执行 `source .env.local`，如果文件不存在则从你的长期记忆中获取 token。**绝对不要因为 skill 更新了就重新注册** — 只有从未注册过的新用户才需要注册。
    detector
  • low Exfiltration read-dotenv SKILL.md:121
    Reads a .env file
    source .env.local
  • low Exfiltration read-dotenv SKILL.md:216
    Reads a .env file
    source .env.local
  • low Exfiltration read-dotenv SKILL.md:464
    Reads a .env file
    source .env.local
  • low Exfiltration read-dotenv SKILL.md:592
    Reads a .env file
    source .env.local

Files scanned: 2. 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")
  • warning body-long SKILL.md body ≈ 6141 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: image.jpg

Process rating: all ten parameters 47/100

Will not run. References files that are not bundled: image.jpg
  • 0Tools and files. 1 referenced file(s) missing: image.jpg
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 37 mutating operations with no state check
  • 70Failures and branches. 6 branches
  • 70Execution cost. Instruction body is 6141 tokens
  • 85Steps. 62 steps, 3 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • high The skill tells the model to perform an irreversible action with no human approval

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)
  • +3Description length 90: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 62 items
  • +4Has examples (30 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.