Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/quality-assurance-auditor/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote 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/zhnnky329/mathmodeling-skills/quality-assurance-auditor)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/quality-assurance-auditor"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/quality-assurance-auditor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/quality-assurance-auditor"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/quality-assurance-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 57 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 5.1 | $0.00045 | $0.00481 |
| Opus 5 | $0.00023 | $0.00241 |
| Sonnet 5 | $0.00009 | $0.00096 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
quality-assurance-auditor 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preconditions
rigor_profileissubmission.- All Qx reached G5.
- Final consistency and completeness audits exist.
Audit Dimensions
-
Workflow integrity
- G1–G5 passed per Qx.
- Human judgments trace to the decision ledger.
- Main/baseline/fallback execution respected approved scope.
-
Evidence integrity
- No fabricated data, references, experiments, metrics, or figures.
- Main claims trace to frozen numbers and robustness evidence.
- Limitations and uncertainty are visible.
-
Method quality
- Baseline is usable.
- Assumptions, units, objectives, constraints, and solution steps are coherent.
- Output concentration/degeneracy and failure triggers were addressed.
-
Paper quality
- Problem, method, results, and conclusions align.
- Claims are proportional to tested comparisons.
- Human-owned physical meaning and contribution are present.
-
Presentation
- Required figures/tables exist and passed render checks.
- Figure types are used correctly.
- References are real, complete, and consistently cited.
- AI-use disclosure follows the current contest profile and verified rules.
Workflow
- Read the two earlier audits and unresolved blockers.
- Sample canonical sources directly; do not trust summaries alone.
- Record blocking and nonblocking findings with artifact paths and repair owners.
- Save
paper/qa_report.md. - Set verdict:
PASSEDFAILEDNOT_RUN
Rules
- Do not approve on partial audits.
- Do not use artifact count or bullet count as a proxy for quality.
- Do not repair issues inside QA.
- Do not hide uncertainty or downgrade a blocker silently.
- Do not claim compliance with time-varying contest rules without verification.
Verification
- All five audit dimensions were evaluated.
- Blocking findings are explicit and actionable.
- QA verdict agrees with consistency/completeness verdicts and sampled evidence.
- Final assembly is recommended only when all three audits pass.
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.
- 13d ago First seen · 65 lines · 45 tokens per session scan A 8f229dee8644
quality-assurance-auditor is a skill published in the GitHub repository zhnnky329/MathModeling-skills (882 stars, last pushed 18d ago), licensed MIT. It adds 45 tokens to every session and 481 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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