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Agent skill

Quality Assurance Auditor

by yushui2022 in yushui2022/MathModel-Skill

强制审计论文生成质量，防止模型偷换、逻辑断链、内容空洞。Invoke when 用户提出检查/审计/验收/确保/verify/QA，或合并前需要把关。

MITAuto-check passedTesting & QA

Install Quality Assurance Auditor

skills CLI
$ npx skills add yushui2022/MathModel-Skill --skill quality-assurance-auditor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install yushui2022/MathModel-Skill quality-assurance-auditor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/yushui2022/MathModel-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/trae/.trae/skills/quality-assurance-auditor .claude/skills/quality-assurance-auditor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
quality-assurance-auditor
GitHub stars
454
Used in
1 other repo
Token cost
~2.1k tokens
SKILL.md length
454 words
Files
3 (incl. scripts)
Skills in repo
10
Repo updated
First seen
Licence
MIT

At a glance

强制审计论文生成质量，防止模型偷换、逻辑断链、内容空洞。Invoke when 用户提出检查/审计/验收/确保/verify/QA，或合并前需要把关。

  • Works in 8 steps: 赛题覆盖度 → 模型-任务匹配度 → 逻辑一致性 → …
  • Tasks that involve QA and bug reports
  • SKILL.md covers 全局流程协作约束（长对话防漂移）, 执行契约, 目标 and 适用时机, plus 11 more sections
  • Runs Python scripts from its folder; calls python

What it does

Quality Assurance Auditor is an agent skill from yushui2022/MathModel-Skill. 强制审计论文生成质量，防止模型偷换、逻辑断链、内容空洞。Invoke when 用户提出检查/审计/验收/确保/verify/QA，或合并前需要把关。

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/evidence_gate.py` and `scripts/pipeline.py`).

It sits in Testing & QA, covering QA and bug reports. The repository describes itself as: Agent-native mathematical modeling workflow skills for Trae, Claude Code, and Codex, covering problem parsing, modeling, code generation, evidence checks, paper writing, and Word… The licence is MIT.

When your agent uses it

  • Tasks that involve QA and bug reports

Example prompts

  • “/quality-assurance-auditor”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. 赛题覆盖度
  2. 模型-任务匹配度
  3. 逻辑一致性
  4. 评分点对齐度
  5. 内容完整性与反灌水（High Priority）
  6. 证据与运行新鲜度
  7. 图表-正文链检测
  8. 正式交付检查

What it can do on your machine

Read from SKILL.md and the folder at commit 7712876. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Quality Assurance Auditor loads about 2.1k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 454 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from yushui2022/MathModel-Skill at commit 7712876, republished under its MIT licence (© yushui2022). 454 words, ~2,053 tokens.

Download SKILL.mdSave it as .claude/skills/quality-assurance-auditor/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
quality-assurance-auditor
description
强制审计论文生成质量，防止模型偷换、逻辑断链、内容空洞。Invoke when 用户提出检查/审计/验收/确保/verify/QA，或合并前需要把关。

质量审计员（Quality Assurance Auditor）

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：
    bash
    python .trae/skills/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：
    bash
    python .trae/skills/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：
    bash
    python .trae/skills/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。

目标

  • 在论文生成的每一个关键节点插入“强制验收点”，只有审计通过才能进入下一步，防止“字数达标但逻辑错误”或“模型偷换”等隐性偷懒。
  • 提供可量化的通过/失败判定，并给出具体修改清单，确保最终论文既“厚”又“对”。
  • 明确区分“quickstart 验证草稿”和“正式比赛稿”：脚本跑通不等于论文合格；结果证据仍为骨架时，不得交付最终稿。
  • 明确职责边界：evidence_gate.py 判断结果是否来自可复核运行、输入与产物是否仍新鲜、指标和图表表格是否可用；paper-formal-writer/scripts/check_paper_format.py 判断动态篇幅、三级标题、原生公式、正文引文、图表引用和 Word 渲染质量。两个门禁都通过后，才能称为正式稿。

适用时机

  • 任何一步 skill（赛题解析、模型选型、结构设计、微单元生成）完成后，用户希望确认“这一步真的做对了吗？”
  • 在合并全文之前，做最终一致性扫描，防止前后矛盾、遗漏约束、图表断链。
  • 当用户怀疑“AI 偷偷简化了模型”或“生成的内容空洞”时，立即调用本 skill 进行强制审计。

输入

  • 必填：待审计产物（如 problem-doc-model-selector 输出的模型路线、论文结构清单、或单个微单元文本）。
  • 必填：原始赛题 PDF/Word 内容或关键约束摘录，用于对照。
  • 可选：用户自定义的评分点清单（如 CUMCM 官方 rubric），可覆盖默认规则。

输出

  • 审计报告（JSON + 人类可读）：
    • checkpoint_id：本次审计节点编号
    • status：PASS / FAIL
    • score：0–100 量化得分
    • failures[]：未通过项列表，含「问题描述」「位置」「建议修改」
    • warnings[]：可接受但建议优化的项
  • 若状态为 FAIL，则拒绝进入下一步，并给出「必须修改清单」
  • 若状态为 PASS，则给出「已验证通过」印章，可安全继续

审计维度（可逐层开关）

  1. 赛题覆盖度
    • 是否 100% 识别所有子问题（Q1/Q2/Q3…）
    • 是否 100% 覆盖所有显式约束（不等式、边界、特殊条件）
  2. 模型-任务匹配度
    • 模型类型（预测/优化/分类）与任务目标是否一致
    • 是否具备处理给定数据格式的能力（时序→时序模型、截面→回归等）
  3. 逻辑一致性
    • 假设 ⇄ 模型推导 ⇄ 结果解释 是否闭环
    • 符号表是否出现冲突或双口径（同一符号两种含义）
  4. 评分点对齐度
    • 每个高分点（敏感性分析、对照实验、误差量化）是否在结构或文本中被“可定位”地承诺
  5. 内容完整性与反灌水（High Priority）
    • 禁止凑字数：复制段落后只替换数字、序号、少量同义词或连接词，均视为机械扩写并直接 FAIL。
    • 拒绝重复和占位：连续重复、数字归一化后近重复、模板占位符和“待补结果”痕迹均直接 FAIL。
    • 结构核查：对照正式 outline 检查核心章节和每问证据是否完整；不能用空泛背景、重复结论或图表描述堆叠代替模型推导。
  6. 证据与运行新鲜度
    • execution_provenance、run_manifest.json、建模脚本、输入文件和输出产物必须相互匹配，记录的 SHA-256 必须等于当前文件。
    • 计算指标必须非空且为有限值；None、空字符串、NaN、Inf 或缺少 result_summary 均直接 FAIL。
    • 图表或表格只要缺失、空文件、生成失败、标记为 placeholder，或包含失败/占位说明，均不得作为正式证据。
  7. 图表-正文链检测
    • 正文是否出现“见图 3”而图 3 真实存在且编号正确
    • 图表标题与正文描述是否语义一致
  8. 正式交付检查
    • Word 导出、原生公式、正文引文、三级标题和渲染质量由 paper-formal-writer 负责；本 skill 不从 final_paper.md 直接拼装正式 Word。
    • 最终验收必须读取 format_check_report.json，确认状态为 PASS、输入哈希仍新鲜且 render_qa.status=PASS。

工作流程（可嵌套到任意阶段）

  1. 接收待审计产物与原始赛题
  2. 按用户指定的维度逐项扫描
  3. 生成 PASS/FAIL 报告与修改清单
  4. 若 FAIL，则阻塞后续 skill 调用，强制返回修改；若 PASS，则盖章放行
  5. 可选：将审计结果写入 .audit_log.jsonl，供后续追溯

使用示例

  • 输入：（1）problem-doc-model-selector 输出的「模型路线」、（2）赛题原文
  • 行为：
    • 检查是否遗漏子问题 → 发现 Q3 未被提及 → FAIL
    • 报告：failures: [{"issue": "子问题 Q3 未被识别", "suggestion": "在模型路线中补充 Q3 的任务拆解与指标定义"}]
    • 用户必须先修正模型路线，重新审计通过后才能进入结构设计

防偷懒机制

  • 本 skill 绝不生成任何新内容，只做「Review & Reject」
  • 所有判定规则公开透明，用户可自定义阈值或开关维度
  • FAIL 时强制阻塞，无法通过 prompt 绕过，必须实质修改
Show full SKILL.md (182 more words)Show less

备注

  • 审计规则默认对齐 CUMCM 国赛评分标准，可通过 rubric= 参数覆盖
  • 支持中英文赛题与输出，关键词库可扩展
  • 审计本身不依赖外部大模型，全部基于规则与关键词库，确保速度与不偏性

附录 A：脚本入口（推荐）

本 skill 的可执行脚本都放在 scripts/ 下。

当前 scripts/pipeline.py 是 legacy/quickstart 基础检查脚本，负责初始化目录、检查 problem_files/ 并按需生成 paper_output/tasks.json。正式 S6 使用 evidence_gate.py，正式 S7 不依赖 tasks.json。

scripts/evidence_gate.py 是正式成稿前证据门禁脚本，负责检查每个 question_id 是否具备真实模型结果、有限且非空的评价指标、可读取的图表或表格、结论回扣和任务追踪。official 模式会复核 execution_provenance 与 paper_output/results/run_manifest.json，比较建模脚本、输入文件和输出产物的当前大小与 SHA-256，拒绝运行后被修改的代码或数据；缺少 result_summary、失败/空/占位图表表格、无匹配运行记录也会失败。它会输出 paper_output/qa/evidence_gate_report.json 与 .md，并在 JSON 的 input_hashes 中记录本次门禁输入，供后续格式化和 workflow guard 检查报告是否过期。official 模式未通过会返回非零退出码；quickstart 模式只给 warning。

paper-formal-writer/scripts/check_paper_format.py 是正式成稿后的格式门禁脚本。它读取 outline 中按子问题数量生成的动态篇幅目标，检查 1 / 1.1 / 1.1.1 三级标题、每问的建模/算法/结果/检验、图表引用、正文引文与参考文献闭环、可编辑 Word OMML 公式、重复段落和内部工程话术。最终交付必须使用 --render required，通过 LibreOffice 将 DOCX 转为 PDF 并验证页数和可提取文本；报告同时记录源稿、DOCX、outline、索引和 evidence report 的哈希。它不替代 evidence_gate.py，而是在证据门禁通过后阻止内容或版式不合格的 Word 被称为最终稿。

  • 若运行 legacy/quickstart 且存在 paper_output/plan/model_route.json，pipeline 会优先按模型路线、评分点证据、主模型、验证计划和建议图表动态生成微单元清单。
  • 若存在 paper_output/plan/data_plan.json、visualization_plan.json 与 paper_output/figure_index.json，脚本会做轻量证据链检查：确认图表 ID、输出路径和数据路径可追溯，但不会因为计划图尚未实际生成就阻塞全流程。
  • 若存在 paper_output/results/model_results.json、metrics.json、conclusions.json 与 paper_output/tables/table_index.json，脚本会把 result_summary、key_metrics、tables、conclusions、evidence_status 写入每个子问题任务，供微单元生成器直接使用。
  • 若不存在模型路线契约但存在 paper_output/step1/problem_analysis.json，脚本会按真实子问题、任务类型、推荐模型、验证计划和建议图表动态生成微单元清单。
  • 若不存在结构化题意分析，脚本才回退到通用任务清单模板。
  • 若 paper_output/tasks.json 已存在，默认不覆盖；需要按最新题意重新生成时，设置 MATHMODEL_REGENERATE_TASKS=1 后再运行。

更细的审计规则写在本 SKILL.md 中，Agent 在真正验收论文时必须结合正文、题面、任务清单和图表引用执行这些规则，而不能只把脚本跑通当作质量通过。

在项目根目录运行：

bash
python .trae/skills/quality-assurance-auditor/scripts/pipeline.py

正式成稿前运行证据门禁：

bash
python .trae/skills/quality-assurance-auditor/scripts/evidence_gate.py

quickstart 验证时只看 warning：

bash
python .trae/skills/quality-assurance-auditor/scripts/evidence_gate.py --mode quickstart

行为：official 模式读取模型路线、真实运行账本、结果、指标、结论、图表和表格，输出带输入哈希的正式证据门禁；pipeline 仅为 legacy/quickstart 生成可选任务清单。

目录约定（与项目全局对齐）

  • 本技能会强制要求 problem_files/ 非空。
  • 本技能统一在 paper_output/qa/ 下产出正式门禁报告；legacy/quickstart 清单仍位于 paper_output/tasks.json。

前后衔接

  • 常作为 S6 全局门禁：正式写作前必须通过。
  • official PASS 后回到 paper-workflow-orchestrator，通常进入 paper-formal-writer；微单元仅处理 S7 明确排队的局部修复。

约束（必须遵守）

  • Memory Interaction (必做):
    • 审计通过后：必须调用 context-memory-keeper，记录证据门禁状态、报告哈希和下一阶段。
  • 本技能是 S6 全局门禁：正式写作依赖 fresh PASS，不依赖任务清单。
  • 正式 paper-micro-unit-generator 入口必须由 repair_queue.json 的 micro-repair 项触发；legacy/quickstart 才依赖 tasks.json。
  • 若 evidence_gate.py 未通过，禁止把 final_paper.docx 称为最终稿；必须回到 model-code-and-result-generator 或当前赛题专用代码，补齐真实结果、指标、图表、表格和结论。
  • 若 paper-formal-writer/scripts/check_paper_format.py --render required 未通过，禁止把 final_paper.docx 称为最终稿；必须回到 final_paper_source.md 或格式化阶段修复动态篇幅、标题结构、图表解释、原生公式、正文引文、重复内容、参考文献、附录或渲染问题。
  • 当用户已生成 paper_output/final_paper_source.md 时，使用 S7 final audit 与 S8 format gate 做最终一致性把关。

© yushui2022, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts) in packages/trae/.trae/skills/quality-assurance-auditor of yushui2022/MathModel-Skill.

  • SKILL.md
  • scripts/evidence_gate.py
  • scripts/pipeline.py

Open the folder on GitHubat commit 7712876

Used in 1 other repository

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in yushui2022/MathModel-Skill, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Quality Assurance Auditor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Quality Assurance Auditor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Quality Assurance Auditor this skillyushui2022/MathModel-Skill4541 repos~2.1kAutomated safety check: PassMIT
Reproduce Chat Statesdifferent-ai/openwork24k—~673Automated safety check: PassCustom licence
Dynamo Jira TicketDynamoDS/Dynamo2k—~1.1kAutomated safety check: PassApache-2.0
Moav E2EMotherofallVPNs/MoaV449—~1.9kAutomated safety check: NotesMIT
Creating A Coral TaskHuman-Agent-Society/CORAL1.1k—~2.2kAutomated safety check: PassApache-2.0
Launch Rlmarin-community/marin3.9k—~894Automated safety check: PassApache-2.0

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    Testing & QAAuto-check passed

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More from yushui2022/MathModel-Skill

All 10 skills in this repo

  • Modeling Paper Rubric and Model Selector

    yushui2022/MathModel-Skill

    Builds a scoring-aligned outline for a mathematical modeling paper and a model selection plan with baseline, improvement and validation experiments.

    454 GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check passed

  • Modeling Code and Result Contracts

    yushui2022/MathModel-Skill

    Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.

    454 GitHub stars~1.4k tokensUpdated 4 days ago
    Auto-check passed

  • Formal Modeling Paper Writer

    yushui2022/MathModel-Skill

    Plans, drafts, audits, formats and verifies a formal mathematical-modeling paper from an evidence chain, delivering audited Markdown and a Word file with native equations.

    454 GitHub stars~1.6k tokensUpdated 4 days ago
    Auto-check passed

  • Paper Micro-Unit Generator

    yushui2022/MathModel-Skill

    Repairs one failing section of a mathematical modeling paper from the repair queue, or builds a legacy or quickstart scaffold when you ask for one by name.

    454 GitHub stars~1.1k tokensUpdated 4 days ago
    Auto-check passed

  • Authoritative Data Harvester

    yushui2022/MathModel-Skill

    Finds authoritative public data sources for modeling tasks, prefers official APIs and bulk downloads, and outputs a reproducible fetch and cleaning plan with citations.

    454 GitHub starsUsed in 1 repo~1.1k tokens
    Auto-check passed

  • Context Memory Keeper

    yushui2022/MathModel-Skill

    Maintains a two-layer persistent memory for a math-modeling paper workflow: long-term rules plus a short-term workbench, with finished tasks archived.

    454 GitHub starsUsed in 1 repo~893 tokens
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Categories

Questions about Quality Assurance Auditor

What does Quality Assurance Auditor do?

强制审计论文生成质量，防止模型偷换、逻辑断链、内容空洞。Invoke when 用户提出检查/审计/验收/确保/verify/QA，或合并前需要把关。. Quality Assurance Auditor is an agent skill from yushui2022/MathModel-Skill.

When should I use Quality Assurance Auditor?

Quality Assurance Auditor fits situations like: tasks that involve QA and bug reports.

How do I install Quality Assurance Auditor in Claude Code?

Run `npx skills add yushui2022/MathModel-Skill --skill quality-assurance-auditor -a claude-code`. Or copy the skill folder (packages/trae/.trae/skills/quality-assurance-auditor in yushui2022/MathModel-Skill) into .claude/skills/quality-assurance-auditor in your project. Claude Code loads it when a task matches its description.

How do I install Quality Assurance Auditor in Codex?

Run `npx skills add yushui2022/MathModel-Skill --skill quality-assurance-auditor -a codex`. Or copy the skill folder (packages/trae/.trae/skills/quality-assurance-auditor in yushui2022/MathModel-Skill) into .agents/skills/quality-assurance-auditor in your project. Codex loads it when a task matches its description.

Can I use Quality Assurance Auditor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yushui2022/MathModel-Skill --skill quality-assurance-auditor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/quality-assurance-auditor, .gemini/skills/quality-assurance-auditor, .github/skills/quality-assurance-auditor and .opencode/skills/quality-assurance-auditor in your project.

What does Quality Assurance Auditor need to run?

Going by SKILL.md and its folder, Quality Assurance Auditor needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Quality Assurance Auditor access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Quality Assurance Auditor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Quality Assurance Auditor use?

Quality Assurance Auditor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Quality Assurance Auditor use?

About 2.1k tokens (SKILL.md is roughly 8.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Quality Assurance Auditor?

Skills that share tags, products or a category with Quality Assurance Auditor: Reproduce Chat States (different-ai/openwork, 24k stars), Dynamo Jira Ticket (DynamoDS/Dynamo, 2k stars), Moav E2E (MotherofallVPNs/MoaV, 449 stars) and Creating A Coral Task (Human-Agent-Society/CORAL, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Quality Assurance Auditor?

yushui2022 (a GitHub user) maintains it in yushui2022/MathModel-Skill, which has 454 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 7, 2026.

Source: yushui2022/MathModel-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.