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BF luban-skill

鲁班.Skill（luban Skill）：工业级智能体技能优化器。当用户提及以下关键词时调用：“优化skill”、“skill评分”、“自动优化”、“auto optimize”、“skill质量检查”、“小鲁班”、“luban”、“优化技能”、“帮我改skill”、“skill怎么样”、“提升skill质量”、“skill review”、“skill打分”。

ClawHub Agent Skills author: ebandao v0.1.0 MIT-0 16 files body ≈ 6 251 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

鲁班.Skill（luban Skill）：工业级智能体技能优化器。当用户提及以下关键词时调用：“优化skill”、“skill评分”、“自动优化”、“auto…

As a process F 31/100 · Will not run — References files that are not bundled: references/baseline-skill.md

AnalyzerAI 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
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: references/baseline-skill.md
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
This is a copy of a skill from another catalog; the rating counts the canonical one: luban-skill (ClawHub)

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 · 0

✓ No critical or high findings

Files scanned: 16. 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 ≈ 6251 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/baseline-skill.md

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: references/baseline-skill.md
  • 0Tools and files. 1 referenced file(s) missing: references/baseline-skill.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (luban-skill) differs from the folder (luban-skill-2)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 6251 tokens
  • 100Steps. 61 steps
  • 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -232 emoji in the instructions: noise for the model
  • -31 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +3Description length 183: enough signal without eating the budget
  • +4Structure: 51 headings
  • +3Step-by-step instructions: 61 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
This skill is a real skill optimizer, but it can edit and persist changes across skill repositories more broadly than its simple review-style triggers make clear.
LLM: suspicious (high) · 15 Aug 2026