SkillAtlasSkill 详情

b2

Security audit: baseline 52/52 CLEAN

审核状态:已审核Quality 80Security 100

复制安装命令

用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。

复制前请先查看来源、License 和安全提示。

项目 README

来源文件:README.md

抓取于 2026年8月6日

Awesome GitHub stars License: CC BY-SA 4.0 PRs Welcome Validate catalog OpenSSF Scorecard Security audit: baseline 52/52 CLEAN Rigor coverage Powered by StatsPAI

Auto-Empirical Research Skills (AERS)

📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。

English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位)

🌐 语言: English | 简体中文(默认) | 繁體中文 | 日本語 | 한국어


CoPaper.AI Stanford REAP - Center on China's Economy & Institutions

Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品
由斯坦福实证研究方法论团队打造,覆盖从数据清洗到顶刊投稿的完整工作流



实证研究智能体技能大全封面图

🚀 New here? Open the Skill Search → to filter all 1,096 skills by method, stage, language, and license. The 5-minute tour (make quickstart) prints the same picture in your terminal.

🇨🇳 中文用户从本文件开始(流水线速览 + 76 行总表),每个合集的完整描述见 docs/CONTENT_ZH.md。📖 English readers: see README-en.md.


信任面 · Trust surface (rigor stats)

Rigor laneCountWhere
Numeric benchmark tasks — gold values recomputed from real data each run17benchmark/
Behavioral eval scenarios / rubric items37 / 183eval-harness/

Full trust overview: docs/TRUST.md · docs/RIGOR_COVERAGE.md


⚡ 安装与使用(30 秒上手)

最省事的一招:把 URL 丢给 Agent

把项目 URL 地址 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills 丢给 Claude Code / Codex,并指定是目录 / 项目 / 全局安装 —— 剩下的让它自己做。例如:

帮我安装 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills
装到「全局」(~/.claude/skills/),我想在所有项目里都能用

把最后一行换成你要的作用域即可:

作用域说给 Agent 的话落到哪里
目录(当前会话临时用)"只在当前目录用,不要全局安装"当前工作目录下的 .claude/skills/
项目(团队共享,可提交进 git)"装到本项目"项目根目录 .claude/skills/
全局(所有项目可用)"装到全局"~/.claude/skills/(Codex 为 ~/.codex/skills/)

手动安装(两种,任选其一)

A. 插件市场(Claude Code v2.1+,推荐,可升级)

claude plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills
claude plugin install aer-skills@auto-empirical-research-skills                 # 顶刊投稿全流程(9 skills)
claude plugin install empirical-analysis-python@auto-empirical-research-skills  # Python 计量流水线
claude plugin install empirical-analysis-stata@auto-empirical-research-skills   # Stata 计量流水线
claude plugin install empirical-analysis-r@auto-empirical-research-skills       # R + Quarto 流水线

B. 只要某一个 skill —— 直接拷文件夹

git clone --recurse-submodules https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
cd Auto-Empirical-Research-Skills

cp -R skills/00.1-Full-empirical-analysis-skill_Python  .claude/skills/   # 项目级
cp -R skills/00.1-Full-empirical-analysis-skill_Python  ~/.claude/skills/ # 全局

拷进去的文件夹必须自带 SKILL.md(部分合集的 SKILL.md 在下一层,拷那一层)。

怎么用

新开一个会话,直接用自然语言说要做什么,Agent 会按 description 自动挑 skill;说不动就点名方法或 skill:

用面板数据跑一个 Callaway–Sant'Anna 事件研究,并出 HonestDiD 稳健性和期刊级表格

完整安装说明(Codex / CodeBuddy 整库导入、--plugin-dir 单次加载、常见故障排查)见 INSTALL.md。


中文文档结构

中文内容分两级维护,各司其职:

  • 本文件(README.md,GitHub 默认入口):banner、badges、信任面、9 阶段流水线速览、76 行合集总表。
  • docs/CONTENT_ZH.md(扩展正文):每个合集的完整描述(#skill-NN 锚点)、按用途分组、精确数字、2 分钟验证、三层信任、旗舰流水线详解、贡献与引用。总表行内的 → 直接跳到对应锚点。
  • 其他语言:README-en.md · README-zh-TW.md · README-ja.md · README-ko.md

[!NOTE] 维护规则: 改合集总表 → 本文件与 CONTENT_ZH.md 的锚点表两处同步;改合集详情 / 分组 / 数字 → 只改 docs/CONTENT_ZH.md。统计数字(合集数 / skill 数)以 catalog/skills.json 为准,由 make validate 的 readme-stats 检查器守护。

贡献者(Contributors): 提交前请在本地跑通完整门禁 make check(catalog 校验 + 链接 + 单元测试 + eval-harness + benchmark)。详见 CONTRIBUTING.md。

旧版归档: README-zh-CN.md 已弃用,仅作向后兼容的重定向占位。


🚀 从一个 idea 到一篇论文:社科实证研究 · 端到端流水线(全自动、可介入)

AERS 不只是 76 个散装 skill —— 它能陪你走完一篇论文。 从模糊 idea → 选题精炼 → 文献综述 → 数据获取 → 识别策略 → 估计建模 → 稳健性审计 → 出版级表格 / 图形 → 写作与同行评审 → 降 AIGC → 投稿。端到端、全自动、每一步都可被人介入(中间任何一步你都可以接过去手工改方法、补变量、加稳健性,再让流水线自动接上跑)。

9 阶段流水线 · 每一步都覆盖到具体 skill

#阶段关键 skills(点合集名进目录,→ 进完整说明)
1️⃣选题精炼 — Agent 把模糊想法收紧成"可证伪 + 可执行"的研究问题· 25 Diverga · 33 claude-scholar · 05 research-superpower · 11 compound-science
2️⃣文献综述 — 检索 · 筛选 · PRISMA 流程 · 批判性阅读 · 主题分析· 36 literature-review-skill · 24 academic-research-skills · 59 openalex-skill · 68 research-productivity-skills · 53 thematic-analysis
3️⃣数据获取 — 公开数据库 · API · 网页抓取 · 数据清洗· 33 claude-scholar · 68 research-productivity-skills · 32 stata-skill · 57 edgartools
4️⃣识别策略 — DiD / RD / IV / SCM / DML / matching 全覆盖· ⭐ 00 StatsPAI 🔥 · 10 causal-inference-mixtape · 13 MixtapeTools · 51 CausalPy · 63 scientific-agent-skills
5️⃣估计建模 — Python / Stata / R 三栈,900+ 估计器· ⭐ 00.1 Full Empirical · Python · ⭐ 00.2 Full Empirical · Stata · ⭐ 00.3 Full Empirical · R · 40 pyfixest · 39 marginaleffects · 09 awesome-econ-ai
6️⃣稳健性审计 — 复现包检查 · Honest-DiD · R&R 模拟· 41 sewage-econometrics-check · ⭐ 50 AER-skills · 21 AI-research-feedback
7️⃣表格 & 图形 — 期刊出版级排版 · LaTeX 嵌入· ⭐ 00 StatsPAI · 07 AI-Research-SKILLs · 33 claude-scholar · 08 latex-document-skill
8️⃣写作 & 同行评审 — LaTeX / Quarto · 仿审稿人 · 校对· 06 stats-paper-writing · 04 scientific-writer · 22 christopherkenny-skills · 38 academic-proofreader · 56 econ-writing-skill · 16 clo-author
9️⃣降 AIGC & 投稿 — 知网 / 万方 / Turnitin / 23 类 AI 痕迹模式· ⭐ 48 de-AIGC-skills 🇨🇳🇬🇧 · 44 humanizer_academic · 45 deslop · 46 stop-slop · 47 avoid-ai-writing · 49 humanize-chinese

🎼 元编排:⭐ 69 Paper-WorkFlow —— 一键串起来

Paper-WorkFlow 是 AERS 的"指挥棒",它把上面 9 个阶段的 skill 串成 一条按键即运行的端到端流水线。 你在 IDE 入口给它一句自然语言:

"开一个新论文项目:空气污染与中国劳动力市场,CS 设计 + 省级面板"

它会自动按顺序调:

  1. ⭐ 00 StatsPAI → sp.csdid(...) 给出 CS-DID 估计草案 + 写出估计方程与识别假设
  2. 33 claude-scholar → 抓变量定义 / 数据源候选 / 相关文献
  3. ⭐ 00 StatsPAI → 真跑 sp.feols(...) + sp.honest_did(...)
  4. 41 sewage-econometrics-check → 10 项复现包审计 + 稳健性体检
  5. ⭐ 00 StatsPAI + 07 AI-Research-SKILLs → 出 Table 1–5 + 期刊级图
  6. 38 academic-proofreader → 通读 + §comment 标"审稿人会挑刺的位置"
  7. 56 econ-writing-skill 起草初稿 + ⭐ 48 de-AIGC-skills 🇨🇳🇬🇧 + 45 deslop 过知网 / Turnitin

任何阶段你都可以手动介入 —— 上一阶段的产物全部落盘(产物-幂等 pipeline),你接过去改方法、补控制、加稳健性,再让流水线自动接下去跑。这就是"全自动 + 可介入"。

🏆 7 个 Stanford REAP × CoPaper.AI 自研 skill —— 是整个流水线的主干

⭐ Skill在流水线里的角色
00 StatsPAI 🔥因果引擎:900+ 函数,sp.causal(...) 一行跑闭环(DID / RD / IV / SCM / DML / matching)
00.1 Full Empirical · Python 📘显式 Python 栈(pandas / statsmodels / linearmodels / pyfixest)
00.2 Full Empirical · Stata 📊显式 Stata 栈(reghdfe / ivreg2 / csdid / sdid / rdrobust)
00.3 Full Empirical · R 📗显式 R 栈(tidyverse / fixest / did / HonestDiD)+ Quarto 渲染
48 de-AIGC-skills 🇨🇳🇬🇧中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方)
50 AER-skills 📕Top-5 经济学投稿套件:识别 → 稳健性 → R&R
69 Paper-WorkFlow 🧭元编排器,把上面 9 个阶段串成一键流水线

为什么挑这 7 个?因为它们的行为都被基准钉死了 —— 不是营销口径,是对着已知答案反复跑过验证过的(17 项数值 benchmark + 37 项行为评测 ↗)。

看到这里 —— 完整 76 行合集目录

↴ 直跳到下方 76 行总表(每个合集带 #skill-NN 锚点)。如果你更关心"这些 skill 怎么用"而不是"有哪些 skill",看 📘 中文唯一权威正文 里的「按用途分组」与「旗舰流水线」两节。


🧰 76 个核心 Skills 合集一览(00 → 72,编号连续无空缺)

打开仓库 → 看见整座库。 全部 76 个合集 · 1,096 个 skill,每一个都已 vendor 进本仓库,由 catalog/skills.json 跟踪。⭐ = Stanford REAP × CoPaper.AI 团队自研的 skill;其余为精选、经安全审计的社区作品。

主题图例 — 🚀 全流程与编排器 · 🎯 因果推断与计量经济学 · 📚 文献与研究设计 · ✍️ 写作 / 编辑 / 去 AIGC · 📑 引用 / 复现 / 同行评审 · 🛠️ 数据 / 工具 / 基础设施

点击【→】 跳转到 docs/CONTENT_ZH.md 中该合集的完整描述;点击合集名 直接打开其目录。

🙏 尊重原作者 — 「来源」列直接链回上游原始仓库(owner/repo)。本仓库里的社区合集都是上游快照:请去原仓库点 star、提 issue、看 LICENSE。完整的许可证与来源置信度审计见 docs/LICENSE_AUDIT.md,机器可读版本在 catalog/provenance.json。

#合集一句话详情来源
⭐ 00StatsPAI 🔥因果引擎 · Agent-native Python DSL:sp.causal(...) 一行跑闭环(DID/RD/IV/SCM/DML,900+ 函数)→brycewang-stanford/StatsPAI
⭐ 00.1Full Empirical · Python 📘显式栈:pandas · statsmodels · linearmodels · pyfixest→⭐ 本仓库
⭐ 00.2Full Empirical · Stata 📊reghdfe · ivreg2 · csdid · sdid · rdrobust 复现包→⭐ 本仓库
⭐ 00.3Full Empirical · R 📗tidyverse · fixest · did · HonestDiD + Quarto 渲染→⭐ 本仓库
01academic-paper-skills大纲 → 手稿写作 + 7 维审稿人模拟→lishix520/academic-paper-skills
02research-skills医学影像综述、提案、论文转幻灯片→luwill/research-skills
03scientific-skills假设生成 + 28 个科学数据库→K-Dense-AI/claude-scientific-skills
04scientific-writer引用管理 + 科学写作→K-Dense-AI/claude-scientific-writer
05research-superpower系统化检索、筛选与引文溯源→kthorn/research-superpower
06stats-paper-writing端到端 LaTeX 统计论文写作→fuhaoda/stats-paper-writing-agent-skills
07AI-Research-SKILLs发表级 ML 图表、LaTeX、引文核验→Orchestra-Research/AI-Research-SKILLs
08latex-document-skill创建 / 编译任意 LaTeX 文档为 PDF→ndpvt-web/latex-document-skill
09awesome-econ-aiPython 面板数据分析(linearmodels)→meleantonio/awesome-econ-ai-stuff
10causal-inference-mixtapeDID / IV / RDD / SCM 模板(Cunningham)→Jill0099/causal-inference-mixtape
11compound-science面向定量社会科学的贝叶斯估计→James-Traina/compound-science
12claude-code-my-workflow提交 → PR → 合并的研究工作流(Emory)→pedrohcgs/claude-code-my-workflow
13MixtapeToolsCunningham 的因果推断工具集与讲义→scunning1975/MixtapeTools
14research-starterR 中的 IV / DiD / RDD,含完整诊断→luischanci/claude-code-research-starter
15social-science-researchR 或 Python 端到端数据分析→Felpix-Studios/social-science-research
16clo-author多代理数据分析(R / Stata / Python)→hsantanna88/clo-author
17DAAF安全意识代理框架(32 条 deny rule)→DAAF-Contribution-Community/daaf
18stata-accounting来自 126 篇 JAR 论文的实测 Stata 范式→jusi-aalto/stata-accounting-research
19vera-economic-intelligence经济情报 / 政策研究情报工作流→CuellarC05/vera-economic-intelligence
20python-econ-skillDSGE / HANK 与定量经济计算→wenddymacro/python-econ-skill
21AI-research-feedback用 AI 同行评审生成结构化反馈→claesbackman/AI-research-feedback
22christopherkenny-skills面向 Quarto(.qmd)的 APSA 风格检查器→christopherkenny/skills
23baygent带护栏的 PyMC / Arviz 贝叶斯工作流→Learning-Bayesian-Statistics/baygent-skills
24academic-research-skills5 审稿人多视角论文评审→Imbad0202/academic-research-skills
25Diverga研究问题精炼器(抗模式坍缩)→HosungYou/Diverga
26scholar统计算法设计与文档→Data-Wise/claude-plugins
27my_claude_skills经济学摘要写作指南→dariia-m/my_claude_skills
28paper-replicate-agent论文复现代理演示→maxwell2732/paper-replicate-agent-demo
29project20XXy可复现手稿 + notebook 项目→quarcs-lab/project20XXy
30zirui-song-claude-skillsZirui Song 的研究辅助 Claude 技能集→zirui-song/claude-skills
31claude-code-skillsPython 面板数据分析→thalysandratos/claude-code-skills
32stata-skill高性能 Stata C/C++ 插件→dylantmoore/stata-skill
33claude-scholar研究全生命周期:选题 → 综述 → 实验 → 审稿回复→Galaxy-Dawn/claude-scholar
34research-companion头脑风暴、评估并决策研究方向→andrehuang/research-companion
35academic-writing-skills面向投稿场所的工业 AI 文献研究→bahayonghang/academic-writing-skills
36literature-review-skill完整文献综述工作流(中文)→taoyunudt/literature-review-skill
37IlanStrauss-ai-skillsIlan Strauss 经济学研究 AI 工作流→IlanStrauss/ai-skills
38academic-proofreader学术校对→peternka/academic_proofreader
39marginaleffects预测、斜率与比较(R / Python)→vincentarelbundock/marginaleffects
40pyfixestPython 中的快速固定效应估计→py-econometrics/pyfixest
41sewage-econometrics-check10 项复现包审计→sticerd-eee/sewage
42ARIS自主「research-in-sleep」代理,端到端→wanshuiyin/Auto-claude-code-research-in-sleep
43research-plugins478 个研究插件:数据可视化、领域、基础设施→wentorai/research-plugins
44humanizer_academic为医学/学术手稿去 AI 味(23 类模式)→matsuikentaro1/humanizer_academic
45deslop去除 AI 写作痕迹(5 维评分)→stephenturner/skill-deslop
46stop-slop三层 AI 痕迹检测与改写→hardikpandya/stop-slop
47avoid-ai-writing审计 → 改写 → 二次审计 AI 味(留痕)→conorbronsdon/avoid-ai-writing
⭐ 48de-AIGC-skills 🇨🇳🇬🇧中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方)→⭐ 本仓库
49humanize-chinese检测并人性化 AI 生成的中文文本→swaylq/humanize-chinese
⭐ 50AER-skills 📕Top-5 经济学投稿套件:识别 → 稳健性 → R&R→brycewang-stanford/AER-skills
51CausalPy贝叶斯准实验(PyMC Labs)→pymc-labs/CausalPy
52slr-prisma系统文献综述,PRISMA 2020→keemanxp/slr-prisma
53thematic-analysisBraun & Clarke 六阶段定性主题分析→keemanxp/thematic-analysis-skill
54open-science-skills引用一致性、DOI 与论据支撑审计→scdenney/open-science-skills
55r-skillsR 中用 brms 做贝叶斯推断→ab604/claude-code-r-skills
56econ-writing-skill综合 50+ 顶级指南的经济学写作→hanlulong/econ-writing-skill
57edgartools查询与分析 SEC 文件→dgunning/edgartools
58econstack政策简报(UK GES / AU Treasury)→charlescoverdale/econstack
59openalex-skill通过 OpenAlex 查询 2.4 亿+ 学术作品→shiquda/openalex-skill
60superpapers综合性实证研究支持套件→regisely/superpapers
61research-methods与预注册匹配的验证性检验→phdemotions/research-methods
62citation-checker对照 CrossRef / S2 / OpenAlex 核验引用→PHY041/claude-skill-citation-checker
63scientific-agent-skillsDoWhy 识别–估计–反驳框架→tondevrel/scientific-agent-skills
64mcp-stata20 个 Stata 因果推断与复现 skill→tmonk/mcp-stata
65game-theory-paper-writer生成并压力测试博弈论论文→本仓库 PR #17
66empirical-research-skills面向大型面板的 R 性能优化→SiyaoZheng/ai4ss-skills
67econfin-workflow-toolkit中国公司金融实证工作流,从提案到论文→本仓库 PR #22
68research-productivity-skills论文检索、SSRN、DOI 查询、下载→本仓库 PR #21
⭐ 69Paper-WorkFlow 🧭元编排器,串起整个社会科学论文流水线→brycewang-stanford/Paper-WorkFlow
70ssci-polish ✍️SSCI / SCI 英文论文语言润色(语法、可读性、学术语气)→⭐ 本仓库
⭐ 71lit-review-agent-tools 🔍文献综述工具选型 + 一键安装运行(MinerU / PaperQA2 / ASReview / STORM / MCP 服务器)→brycewang-stanford/lit-review-agent-tools
⭐ 72Kaggle Research 🧪通过官方 CLI 安全检索 Kaggle 资源、限界下载公开数据并保留审计证据→⭐ 本仓库

想看更详细的描述(主题分类、字段、统计)? 见 docs/CONTENT_ZH.md 中标注 #skill-NN 锚点的同一张表 —— 它是每个合集的完整描述所在的扩展正文。

📈 项目历程

自 2026-04 首次发布以来的主干里程碑(完整提交记录见 Commits 与 CHANGELOG.md):

---
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    rotateCommitLabel: false
---
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内置 20 个方法论 skill · 20 分钟完成实证论文 · 自研 StatsPAI(900+ 函数 / MIT 开源)

其他

低风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:0 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/25-HosungYou-Diverga/skills/b2" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/25-HosungYou-Diverga/skills/b2" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/25-HosungYou-Diverga/skills/b2" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/25-HosungYou-Diverga/skills/b2" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git
  3. 将 "skills/25-HosungYou-Diverga/skills/b2" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: b2
description: |
  VS-Enhanced Evidence Quality Appraiser - Prevents Mode Collapse with context-adaptive quality assessment
  Enhanced VS 3-Phase process: Avoids automatic tool application, delivers research-specific evaluation strategies
  Use when: appraising study quality, assessing risk of bias, grading evidence
  Triggers: quality appraisal, RoB, GRADE, Newcastle-Ottawa, risk of bias, methodological quality
version: "12.0.1"

⛔ Prerequisites (v8.2 — MCP Enforcement)

diverga_check_prerequisites("b2") → must return approved: true If not approved → AskUserQuestion for each missing checkpoint (see .claude/references/checkpoint-templates.md)

Checkpoints During Execution

  • 🟠 CP_QUALITY_REVIEW → diverga_mark_checkpoint("CP_QUALITY_REVIEW", decision, rationale)

Fallback (MCP unavailable)

Read .research/decision-log.yaml directly to verify prerequisites. Conversation history is last resort.


Evidence Quality Appraiser

Agent ID: 06 Category: B - Literature & Evidence VS Level: Enhanced (3-Phase) Tier: Core Icon: 🔬

Overview

Systematically evaluates methodological quality and risk of bias in individual studies. Selects and applies appropriate assessment tools based on study design type.

Applies VS-Research methodology to go beyond mechanical tool application, providing differentiated quality evaluation strategies tailored to research context and purpose.

VS-Research 3-Phase Process (Enhanced)

Phase 1: Modal Quality Assessment Approach Identification

Purpose: Recognize limitations of mechanical tool application

⚠️ **Modal Warning**: The following are the most predictable quality assessment approaches:

| Modal Approach | T-Score | Limitation |
|----------------|---------|------------|
| "RCT → Apply RoB 2.0" | 0.90 | Automatic matching ignoring context |
| "Observational → Apply NOS" | 0.88 | Ignores tool limitations |
| "Report GRADE rating only" | 0.85 | Rating rationale unclear |

➡️ Tool application is baseline. Proceeding with context-adaptive assessment.

Phase 2: Context-Adaptive Evaluation Strategy

Purpose: Present evaluation approaches suited to research purpose and context

**Direction A** (T ≈ 0.7): Standard tool + contextual interpretation
- Standard tool application + domain-specific weighting
- Suitable for: General systematic reviews

**Direction B** (T ≈ 0.4): Multi-tool triangulation
- Simultaneous application of multiple tools + discrepancy analysis
- Additional field-specific quality criteria
- Suitable for: Methodology papers, high-quality reviews

**Direction C** (T < 0.3): Purpose-specific evaluation
- Differentiated criteria by meta-analysis purpose
- Propose new evaluation dimensions (reproducibility, transparency)
- Suitable for: Methodological innovation, guideline development

Phase 4: Recommendation Execution

Based on selected evaluation strategy:

  1. State tool selection rationale
  2. Domain-specific detailed assessment + interpretive commentary
  3. Meta-analysis utilization recommendations
  4. Sensitivity analysis necessity determination

Quality Assessment Typicality Score Reference Table

T > 0.8 (Modal - Supplementation Required):
├── Study type → Standard tool automatic matching
├── Yes/No per checklist item
├── Report only total score or rating
└── Judgment rationale unclear

T 0.5-0.8 (Established - Add Interpretation):
├── Specific rationale per domain
├── Interpret meaning in research context
├── Meta-analysis inclusion/exclusion recommendation
└── Sensitivity analysis necessity determination

T 0.3-0.5 (In-depth - Recommended):
├── Multi-tool triangulation
├── Additional field-specific criteria
├── Quality-effect size relationship analysis
└── Rating uncertainty quantification

T < 0.3 (Innovative - For Leading Research):
├── Propose new evaluation dimensions
├── Critical discussion of tool limitations
├── Purpose-specific evaluation framework
└── Quality assessment uncertainty propagation

When to Use

  • Evaluating included studies in systematic reviews
  • Verifying study quality before meta-analysis
  • Assessing evidence for evidence-based decision making
  • Judging reliability of research findings

Core Functions

  1. Study Type-Specific Tool Selection

    • RCT: Cochrane Risk of Bias 2.0
    • Observational studies: Newcastle-Ottawa Scale, ROBINS-I
    • Qualitative studies: CASP, JBI Critical Appraisal
    • Mixed methods: MMAT
  2. Risk of Bias Assessment

    • Domain-specific bias evaluation
    • Overall risk of bias judgment
    • Evidence-based determination
  3. GRADE Certainty Rating

    • Certainty of evidence assessment
    • Identify upgrade/downgrade factors
    • Support recommendation strength judgment
  4. Quality Summary Visualization

    • Traffic light plot
    • Summary of findings table

Assessment Tool Library

RCT: Cochrane Risk of Bias 2.0

DomainAssessment Content
D1Bias arising from randomization process
D2Bias due to deviations from intended interventions
D3Bias due to missing outcome data
D4Bias in measurement of outcome
D5Bias in selection of reported result

Judgment: Low risk / Some concerns / High risk

Observational Studies: Newcastle-Ottawa Scale

DomainItemsPoints
SelectionRepresentativeness of exposed cohort★
Selection of non-exposed cohort★
Ascertainment of exposure★
Demonstration outcome not present at start★
ComparabilityComparability of cohorts★★
OutcomeAssessment of outcome★
Adequate follow-up length★
Adequacy of follow-up★

Total Score: /9 points

Qualitative Studies: CASP Checklist

  1. Was there a clear statement of aims?
  2. Is a qualitative methodology appropriate?
  3. Was the research design appropriate?
  4. Was the recruitment strategy appropriate?
  5. Was data collected in a way that addressed the research issue?
  6. Has the researcher-participant relationship been considered?
  7. Have ethical issues been considered?
  8. Was data analysis sufficiently rigorous?
  9. Is there a clear statement of findings?
  10. Is the research valuable?

Input Requirements

Required:
  - study_type: "RCT, cohort, case-control, qualitative, etc."
  - study_information: "Methods section or full paper"

Optional:
  - assessment_tool: "If specific tool preferred"
  - assessment_purpose: "Meta-analysis, guideline development, etc."

Output Format

## Study Quality Assessment Report

### 1. Study Information
- Authors: [Author names]
- Year: [Publication year]
- Study Type: [Design type]
- Applied Tool: [Assessment tool name]

### 2. Risk of Bias Assessment (RCT Example)

| Domain | Judgment | Rationale |
|--------|----------|-----------|
| D1: Randomization process | 🟢/🟡/🔴 | [Specific rationale] |
| D2: Deviations from interventions | 🟢/🟡/🔴 | [Specific rationale] |
| D3: Missing outcome data | 🟢/🟡/🔴 | [Specific rationale] |
| D4: Outcome measurement | 🟢/🟡/🔴 | [Specific rationale] |
| D5: Selection of reported result | 🟢/🟡/🔴 | [Specific rationale] |

**Overall Judgment**: [Low risk / Some concerns / High risk]

### 3. Quality Assessment Summary

**Key Strengths:**
1. [Strength 1]
2. [Strength 2]

**Key Weaknesses:**
1. [Weakness 1]
2. [Weakness 2]

### 4. Evidence Utilization Recommendations

- Meta-analysis inclusion: [Recommended/Caution needed/Exclude recommended]
- Sensitivity analysis: [Needed/Not needed]
- Interpretation caveats: [Specific cautions]

### 5. GRADE Assessment (If Applicable)

| Factor | Assessment | Impact |
|--------|------------|--------|
| Study design | | |
| Risk of bias | | ↓ |
| Inconsistency | | |
| Indirectness | | |
| Imprecision | | |
| Publication bias | | |

**Certainty Rating**: ⊕⊕⊕⊕ High / ⊕⊕⊕◯ Moderate / ⊕⊕◯◯ Low / ⊕◯◯◯ Very Low

Prompt Template

You are a research quality assessment expert.

Please evaluate the methodological quality of the following study:

[Study Type]: {study_type}
[Study Information]: {study_info}

Tasks to perform:

[For RCT - Cochrane RoB 2.0]
1. Bias arising from randomization process
2. Bias due to deviations from intended interventions
3. Bias due to missing outcome data
4. Bias in measurement of outcome
5. Bias in selection of reported result
→ Overall judgment: Low / Some concerns / High

[For Observational - Newcastle-Ottawa Scale]
1. Selection - 4 points
2. Comparability - 2 points
3. Outcome/Exposure - 3 points
→ Total: /9

[For Qualitative - CASP]
1. Clear research aim
2. Appropriate qualitative methodology
3. Appropriate research design
... (10 items)

Final output:
- Quality assessment summary table
- Key strengths and weaknesses
- Evidence utilization caveats

GRADE Rating Determination Guide

Downgrade Factors

FactorCriteriaDowngrade
Risk of biasSerious limitations-1 or -2
InconsistencyI² > 75%, CI non-overlap-1 or -2
IndirectnessPICO mismatch-1 or -2
ImprecisionOIS not met, wide CI-1 or -2
Publication biasFunnel plot asymmetry-1

Upgrade Factors (Observational Studies)

FactorCriteriaUpgrade
Large effect sizeRR > 2 or < 0.5+1
Dose-responseClear gradient+1
ConfoundingActs toward reducing effect+1

Extraction Quality Validation (V7 Lesson)

Statistical Consistency Checks

CheckRuleAlert
F-to-t consistencyF(1, df) = t^2Error if >5% deviation
Standardization detection"standardized" in measureCritical flag
Pre-test as outcomePre-test used as ESREJECT
Missing correlationGain score needs r_pre_postWarning

Effect Size Quality Rating

RatingCriteria
HIGHReported g with n, verified calculation
MEDIUMCalculated from M/SD, needs verification
LOWEstimated from t/F/p, high uncertainty
UNACCEPTABLEPre-test as outcome, missing key data

Quality Validation Checklist

extraction_quality_checklist:
  - item: "Source verification"
    check: "ES matches original paper values"
    required: true
  - item: "Calculation verification"
    check: "d-to-g conversion within tolerance"
    required: true
  - item: "Independence check"
    check: "No pre-test as outcome"
    required: true
  - item: "Design classification"
    check: "Between/within/mixed correctly identified"
    required: true
  - item: "Dependency documentation"
    check: "Multiple ES from same study flagged"
    required: true

Related Agents

  • 05-systematic-literature-scout: Search for studies to evaluate
  • 07-effect-size-extractor: Extract effect sizes from quality-assessed studies
  • 14-checklist-manager: Checklist-based assessment support

v3.0 Creativity Mechanism Integration

Available Creativity Mechanisms (ENHANCED)

MechanismApplication TimingUsage Example
Forced AnalogyPhase 2Apply quality criteria from other fields by analogy
Iterative LoopPhase 24-round divergence-convergence for strategy refinement
Semantic DistancePhase 2Discover new evaluation dimensions beyond existing tools

Checkpoint Integration

Applied Checkpoints:
  - CP-INIT-002: Select creativity level
  - CP-VS-001: Select quality assessment direction (multiple)
  - CP-VS-003: Final assessment strategy satisfaction confirmation
  - CP-SD-001: Concept combination distance threshold

Module References

../../research-coordinator/core/vs-engine.md
../../research-coordinator/core/t-score-dynamic.md
../../research-coordinator/creativity/forced-analogy.md
../../research-coordinator/creativity/iterative-loop.md
../../research-coordinator/creativity/semantic-distance.md
../../research-coordinator/interaction/user-checkpoints.md

References

  • VS Engine v3.0: ../../research-coordinator/core/vs-engine.md
  • Dynamic T-Score: ../../research-coordinator/core/t-score-dynamic.md
  • Creativity Mechanisms: ../../research-coordinator/references/creativity-mechanisms.md
  • Project State v4.0: ../../research-coordinator/core/project-state.md
  • Pipeline Templates v4.0: ../../research-coordinator/core/pipeline-templates.md
  • Integration Hub v4.0: ../../research-coordinator/core/integration-hub.md
  • Guided Wizard v4.0: ../../research-coordinator/core/guided-wizard.md
  • Auto-Documentation v4.0: ../../research-coordinator/core/auto-documentation.md
  • Cochrane Handbook Chapter 8: Risk of Bias
  • Sterne et al. (2019). RoB 2 Guidelines
  • Wells et al. Newcastle-Ottawa Scale
  • GRADE Handbook

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