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BF jinguyuan-dumpling-skill

金谷园饺子馆信息查询与在线排队取号。通过金谷园官方 API 查询店铺、排队和菜品信息；内置真实排队动作仅用于在线取号、本人排队进度查询、取消排队。

ClawHub Agent Skills author: 金谷园饺子馆 v3.2.1 MIT-0 9 files body ≈ 1 386 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 35/100 · Will not run — References files that are not bundled: 绝对路径

IntegrationInfrastructuretype 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
86/100
safety, quality, tests
Safety 60%
99
Quality 40%
66
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: 绝对路径
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 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/jgy.cjs:1182
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    var kanji = "(?:[u3000-u303F]|[u3040-u309F]|[u30A0-u30FF]|[uFF00-uFFEF]|[u4E00-u9FAF]|[u2605-u2606]|[u2190-u2195]|u203B|[u201…260]|[u0391-u0451]|[u00A…8u0
    detector

Files scanned: 9. 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 missing-ref reference to a missing file: 绝对路径
  • note frontmatter-key unknown frontmatter key "alwaysApply"
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: 绝对路径
  • 0Tools and files. 1 referenced file(s) missing: 绝对路径
  • 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
  • 100Steps. 41 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1386 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (13 tags): a typed call is more reliable

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 73: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
The skill mostly matches its restaurant query and queueing purpose, but it gives an external API and runtime tool output too much ability to steer the agent's behavior.
LLM: suspicious (high) · 28 Aug 2026