MathModeling-skills is a collection of Claude Code and Codex skills for mathematical modeling competitions, guiding users through data analysis, method-specific checks, reproducible computations, figures, and paper audits without taking over modeling decisions. It is for contestants who want AI to handle mechanical checks while they retain responsibility for choosing methods and interpreting results. The catalogue entries form the project's skills, instructions, hook, setting, and plugin workflow.
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 5.5 | $0.00018 | $0.00192 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 481 once invoked, about $0.0002 per session on Opus 5.5. 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.
quality-assurance-auditor compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| quality-assurance-auditorzhnnky329/MathModeling-skills | Skill | 1.1k | 1mo ago | MIT | A | 45 | Claude Code |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |