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BB quality-nonconformance

Quality control and non-conformance management for regulated manufacturing (FDA 21 CFR 820, IATF 16949, AS9100): NCR lifecycle and disposition, 5-Why/Ishikawa/fault-tree/8D root cause analysis, CAPA systems, SPC interpretation, AQL sampling, and supplier quality audits. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 7 389 tokens Open the sourcegithub.com↗ analyzed 22 h ago

Quality control and non-conformance management for regulated manufacturing (FDA 21 CFR 820, IATF 16949, AS9100): NCR lifecycle and disposition…

As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, failures and branches, progress reporting

ProcedureQuality controltype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7389 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 67/100

  • 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
  • 50When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 7389 tokens
  • 85Steps. 87 steps, 2 vague phrases
  • 100Tools and files. No external tools needed
  • 100Result and completion. Output format and completion criterion are stated
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • low 11 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +2Single-language instructions
  • +3Description length 419: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 87 items
  • +3Output format is stated explicitly
  • +4Has examples (0 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.