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BF CP控制计划技能

提供CP控制计划模板生成、数据分析、风险预警、可视化及版本管理指导；当质量工程师需要编制/优化控制计划、执行过程能力分析或识别质量风险时使用

ClawHub Agent Skills author: engicool v0.1.0 MIT-0 6 files body ≈ 891 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

提供CP控制计划模板生成、数据分析、风险预警、可视化及版本管理指导；当质量工程师需要编制/优化控制计划、执行过程能力分析或识别质量风险时使用

As a process F 31/100 · Will not run — References files that are not bundled: scripts/cp_data_analyzer.py, scripts/cp_risk_analyzer.py, scripts/cp_visualizer.py

ProcedureQuality controltype 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
80/100
safety, quality, tests
Safety 60%
100
Quality 40%
50
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: scripts/cp_data_analyzer.py, scripts/cp_risk_analyzer.py, scripts/cp_visualizer.py
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 · 0

✓ No critical or high findings

Files scanned: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/cp_data_analyzer.py
  • warning missing-ref reference to a missing file: scripts/cp_risk_analyzer.py
  • warning missing-ref reference to a missing file: scripts/cp_visualizer.py
  • warning missing-ref reference to a missing file: references/cp_template_guide.md
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: scripts/cp_data_analyzer.py, scripts/cp_risk_analyzer.py, scripts/cp_visualizer.py
  • 0Tools and files. 4 referenced file(s) missing: scripts/cp_data_analyzer.py, scripts/cp_risk_analyzer.py, scripts/cp_visualizer.py
  • 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 (CP控制计划技能) differs from the folder (skill-cp-control-plan)
  • 100Steps. 67 steps
  • 100Execution cost. Instruction body is 891 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

  • +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 70: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 67 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.

External checks

ClawHub: clean
This skill is a coherent quality-control analysis helper that reads user-selected data files and writes local analysis outputs without hidden network, credential, or persistence behavior.
LLM: benign (high) · VirusTotal: · 16 Jul 2026