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BD 质量计划技能

为通用制造行业产品生成完整质量计划文档，涵盖质量目标、管控流程、检测标准、风险评估等12个模块，支持大纲确认与多轮修改，输出Markdown和HTML两种企业级排版格式；当用户需要制定产品质量计划、质量控制方案、质量管理文件或质量规划文档时使用

ClawHub Agent Skills author: engicool v0.1.0 MIT-0 6 files body ≈ 1 236 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

为通用制造行业产品生成完整质量计划文档，涵盖质量目标、管控流程、检测标准、风险评估等12个模块，支持大纲确认与多轮修改，输出Markdown和HTML两种企业级排版格式；当用户需要制定产品质量计划、质量控制方案、质量管理文件或质量规划文档时使用

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
D
49/100
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

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: 2. 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 49/100

  • 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 (质量计划技能) differs from the folder (skill-quality-plan)
  • 100Tools and files. No external tools needed
  • 100Steps. 64 steps
  • 100Execution cost. Instruction body is 1236 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 122: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 64 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This is a coherent quality-plan document generator that locally creates the requested Markdown and HTML files, with no evidence of hidden exfiltration or destructive behavior.
LLM: benign (high) · VirusTotal: · 16 Jul 2026