quality-assurance-auditor

quality-assurance-auditor is a skill for Codex from yushui2022/MathModel-Skill. It costs 82 tokens per session (4,384 once invoked), scanned A, original, MIT.

A quality audit workflow for generated research papers. It checks whether the chosen models, reasoning, and content remain connected and sufficiently detailed.

In plain words
What is it for?
Use it to inspect, verify, audit, or approve generated papers before merging or delivery.
Why use it?
It helps catch changed models, broken reasoning links, and empty or unsupported sections before a paper is accepted or combined.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to inspect, verify, audit, or approve generated papers before merging or delivery.

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Install with agentmods
npx agentmods add skills/yushui2022/mathmodel-skill/quality-assurance-auditor
Install

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.

Any agent
npx skills add yushui2022/MathModel-Skill --skill quality-assurance-auditor
Clone the repo
git clone --depth 1 https://github.com/yushui2022/MathModel-Skill

Made for: Codex.

Wrote 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.

agentmods badge for quality-assurance-auditor

README.md
[![agentmods](https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/quality-assurance-auditor/github.svg)](https://agentmods.dev/skills/yushui2022/mathmodel-skill/quality-assurance-auditor)
Your own site
<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/quality-assurance-auditor"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/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.

agentmods 80×15 button for quality-assurance-auditor

Your own site · 80×15
<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/quality-assurance-auditor"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/quality-assurance-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,384 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.04384
Opus 5.5 $0.00033 $0.01754
Sonnet 5.5 $0.00016 $0.00877
Haiku 4.5 $0.00008 $0.00438

Measured 6d ago against content hash f138aee790c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 6d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/evidence_gate.py, scripts/evidence_policy.py, scripts/pipeline.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/quality-assurance-auditor/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

质量审计员(Quality Assurance Auditor)

Resolve <SKILL_ROOT> to the parent directory of this loaded skill folder, using its actual absolute path. Run commands from the contest project root, never from the installed skill directory. The placeholder is not a shell environment variable.

全局流程协作约束(长对话防漂移)

  • 本 skill 不得作为孤立入口。用户要求完整论文、生成 Word、继续流程或不确定阶段时,先回到 paper-workflow-orchestrator 判断当前 S0-S8 阶段。
  • 启动或继续本 skill 的正式任务前,必须运行:
    python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --skill quality-assurance-auditor
    
  • 如果输出 [WORKFLOW FAIL] 或报告 status != "PASS",停止本 skill,按 paper_output/qa/workflow_guard_report.json 的失败项回补前置阶段,不得凭记忆继续。
  • 本 skill 只写入自己契约范围内的 paper_output/ 产物;完成后必须回到 paper-workflow-orchestrator 判断下一步,并用 context-memory-keeper 记录已完成产物、阻塞项和下一步。
  • 长对话中如果上下文变长、阶段不确定或用户分开调用 skill,先运行:
    python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --status
    
    再读取 paper_output/qa/workflow_guard_report.json、paper_output/preflight_report.json、paper_output/input_manifest.json、paper_output/results/run_manifest.json 和本 skill 的上游 JSON 契约,按报告里的 recommended_skill 与 next_action 继续。
  • 继续流程前,必须把 paper_output/context/workflow_memory.json 视为长期断点记录;若其中的 current_step、next_step、recommended_skill 与 workflow_guard.py --status 不一致,以 guard 报告为准。
  • 每次完成本 skill 的产物后,先回到 paper-workflow-orchestrator 或运行 workflow_guard.py --status,再更新 workflow memory:
    python "<SKILL_ROOT>/context-memory-keeper/scripts/update_workflow_memory.py"
    
    更新后读取 paper_output/context/workflow_memory.json / .md,确认下一步和推荐 skill 已记录。

执行契约

  • 上游输入:优先读取 paper_output/plan/model_route.json、rubric_alignment.json、data_plan.json、visualization_plan.json、paper_output/figure_index.json、paper_output/results/model_results.json、paper_output/results/run_manifest.json、paper_output/results/metrics.json、paper_output/results/conclusions.json 与 paper_output/tables/table_index.json;缺失时回退到 paper_output/step1/problem_analysis.json。
  • 正式成稿前必须输出 paper_output/qa/evidence_gate_report.json 与 .md,机器可读报告必须记录本次审计输入的 SHA-256。paper_output/tasks.json 仅是 legacy/quickstart 的可选清单,不属于 S7 正式契约。
  • 下游交接:official evidence gate PASS 后直接进入 paper-formal-writer 的自适应章节写作。paper-micro-unit-generator 只有在 S7 修复队列明确要求 micro-repair 时才参与正式流程。
  • 推荐下一步:回到 paper-workflow-orchestrator,由 guard 路由到 paper-formal-writer;不得把 legacy/quickstart 合并稿当作正式稿审计通过。
  • 失败回退:若 problem_files/ 为空应阻塞;若模型路线缺失则用题意分析生成任务;若题意分析也缺失才使用通用任务模板;若正式 evidence gate 缺少 run_manifest.json 或运行记录不匹配,必须回退到 model-code-and-result-generator 重新运行建模代码。

Read the full file on GitHub · 175 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 6d ago Changed · +1 lines · +26 tokens per session f138aee790c9
  2. 8d ago Changed · +7 lines 7d106ed9a6c1
  3. 1mo ago Changed d96521c3244b
  4. 1mo ago First seen · 167 lines · 56 tokens per session scan A a100e9f396c4

Subscribe to this mod's changes

quality-assurance-auditor is a skill published in the GitHub repository yushui2022/MathModel-Skill (456 stars, last pushed 3d ago), licensed MIT. It adds 82 tokens to every session and 4,384 once invoked, about $0.0003 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.

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