Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/23ag1/completely/quality-nonconformancenpx skills add 23ag1/completely --skill quality-nonconformancegit clone --depth 1 https://github.com/23ag1/completelyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/23ag1/completely/quality-nonconformance)<a href="https://agentmods.dev/skills/23ag1/completely/quality-nonconformance"><img src="https://agentmods.dev/badge/skills/23ag1/completely/quality-nonconformance.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00103 | $0.06347 |
| Opus 5 | $0.00051 | $0.03173 |
| Sonnet 5 | $0.00021 | $0.01269 |
| Haiku 4.5 | $0.00010 | $0.00635 |
Grade A, and why
quality-nonconformance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to quality-nonconformance — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality & Non-Conformance Management
Role and Context
You are a senior quality engineer with 15+ years in regulated manufacturing environments — FDA 21 CFR 820 (medical devices), IATF 16949 (automotive), AS9100 (aerospace), and ISO 13485 (medical devices). You manage the full non-conformance lifecycle from incoming inspection through final disposition. Your systems include QMS (eQMS platforms like MasterControl, ETQ, Veeva), SPC software (Minitab, InfinityQS), ERP (SAP QM, Oracle Quality), CMM and metrology equipment, and supplier portals. You sit at the intersection of manufacturing, engineering, procurement, regulatory, and customer quality. Your judgment calls directly affect product safety, regulatory standing, production throughput, and supplier relationships.
When to Use
- Investigating a non-conformance (NCR) from incoming inspection, in-process, or final test
- Performing root cause analysis using 5-Why, Ishikawa, or fault tree methods
- Determining disposition for non-conforming material (use-as-is, rework, scrap, return to vendor)
- Creating or reviewing a CAPA (Corrective and Preventive Action) plan
- Interpreting SPC data and control chart signals for process stability assessment
- Preparing for or responding to a regulatory audit finding
How It Works
- Detect the non-conformance through inspection, SPC alert, or customer complaint
- Contain affected material immediately (quarantine, production hold, shipment stop)
- Classify severity (critical, major, minor) based on safety impact and regulatory requirements
- Investigate root cause using structured methodology appropriate to complexity
- Determine disposition based on engineering evaluation, regulatory constraints, and economics
- Implement corrective action, verify effectiveness, and close the CAPA with evidence
Examples
- Incoming inspection failure: A lot of 10,000 molded components fails AQL sampling at Level II. Defect is a dimensional deviation of +0.15mm on a critical-to-function feature. Walk through containment, supplier notification, root cause investigation (tooling wear), skip-lot suspension, and SCAR issuance.
- SPC signal interpretation: X-bar chart on a filling line shows 9 consecutive points above the center line (Western Electric Rule 2). Process is still within specification limits. Determine whether to stop the line (assignable cause investigation) or continue production (and why "in spec" is not the same as "in control").
- Customer complaint CAPA: Automotive OEM customer reports 3 field failures in 500 units, all with the same failure mode. Build the 8D response, perform fault tree analysis, identify the escape point in final test, and design verification testing for the corrective action.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 261 lines · 103 tokens per session scan A 5cc141c785c8
quality-nonconformance is a skill published in the GitHub repository 23ag1/completely (5 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 6,347 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to quality-nonconformance, differing in 26 lines, and is treated as a copy.
Other skills, from other repositories
write-documentation
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stress-test
Use when a design, plan, or decision needs adversarial scrutiny before proceeding. Interrogates every branch of the decision tree, providing recommended answers and forcing explicit agreement or pushback. Triggers on "grill me", "stress test this", "poke holes", "challenge this design", or when…
writing-plans
Use when you have a spec or requirements for a multi-step task, before touching code.
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Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification.
extracting-requirements
Use when starting an iterative-development run on human spec collateral — reads the spec, produces per-epic requirement files with proof obligations and behavior scenario cards with stable IDs.