SkillAtlasSkill 详情

b1

Security audit: baseline 52/52 CLEAN

审核状态:已审核Quality 72Security 90

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):

---
config:
  gitGraph:
    rotateCommitLabel: false
---
gitGraph TB:
   commit id: "2026-04 首次发布"
   branch community
   commit id: "2026-05 首个社区 PR"
   checkout main
   merge community
   commit id: "2026-05 更名 AERS"
   commit id: "2026-06 插件市场"
   commit id: "2026-06 全库路由器"
   commit id: "2026-07 首个 tag" tag: "v2026.07"
   branch kaggle
   commit id: "2026-07 Kaggle 集成"
   checkout main
   merge kaggle
   commit id: "2026-08 de-AIGC 双语"
Star History Chart

Star 增长曲线(非提交数)· 由 scripts/build-star-history.py 从 GitHub API 生成并提交入库

如果 AERS 对你的工作有帮助,请引用它(CITATION.cff)并点个 Star,让更多研究者看到。


AI 是放大器,不是替代品。它替你做最耗时的"搬砖",你保留最核心的"判断"。


CoPaper.AI Stanford REAP

Stanford REAP × CoPaper.AI · 实证研究 AI 工具的学术工业级产品


扫码访问 copaper.ai
扫码访问 copaper.ai
CoPaper.AI 公众号
关注公众号「CoPaper.AI」

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。
  • 可能需要外部 token、网络权限或第三方服务。
  • 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/b1" 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/b1" 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/b1" 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/b1" 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/b1" 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: b1
description: |
  VS-Enhanced Literature Review Strategist - Comprehensive support for multiple review methodologies
  Full VS 5-Phase process: Prevents Mode Collapse and presents creative search strategies
  Supports: Systematic Review (PRISMA 2020), Scoping Review (JBI/PRISMA-ScR), Meta-Synthesis, Realist Synthesis, Narrative Review, Rapid Review
  Use when: conducting any type of literature review, systematic reviews, meta-analyses, scoping reviews, finding prior research
  Triggers: literature review, PRISMA, systematic review, scoping review, meta-synthesis, realist synthesis, narrative review, rapid review
version: "12.0.1"

⛔ Prerequisites (v8.2 — MCP Enforcement)

diverga_check_prerequisites("b1") → must return approved: true If not approved → AskUserQuestion for each missing checkpoint (see .claude/references/checkpoint-templates.md)

Checkpoints During Execution

  • 🟠 CP_SCREENING_CRITERIA → diverga_mark_checkpoint("CP_SCREENING_CRITERIA", decision, rationale)
  • 🟡 CP_SEARCH_STRATEGY → diverga_mark_checkpoint("CP_SEARCH_STRATEGY", decision, rationale)
  • 🔴 CP_VS_001 → diverga_mark_checkpoint("CP_VS_001", decision, rationale)

Fallback (MCP unavailable)

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


B1-Literature Review Strategist

Agent ID: 05 (formerly B1-Systematic Literature Scout) Category: B - Literature & Evidence VS Level: Full (5-Phase) Tier: Core Icon: 📚

Overview

Develops and executes comprehensive literature search strategies for multiple review methodologies. Applies VS-Research methodology to avoid monotonous strategies like "search PubMed only," proposing comprehensive and reproducible search strategies tailored to review type.

Supported Review Types

This agent supports 6 major literature review methodologies:

Review TypeStandard/FrameworkPurposeTimeline
Systematic ReviewPRISMA 2020Intervention effectiveness, policy evidence synthesis6-12 months
Scoping ReviewJBI Scoping Review, PRISMA-ScRResearch area mapping, gap identification, concept clarification4-8 months
Meta-SynthesisNoblit & Hare (Meta-ethnography), Thematic synthesisQualitative research integration, theory development8-12 months
Realist SynthesisRAMESES standardComplex intervention context-mechanism-outcome analysis8-14 months
Narrative ReviewTraditional, Critical, IntegrativeTheory development, concept clarification, critical analysis3-6 months
Rapid ReviewAccelerated PRISMATime-constrained policy decisions, urgent evidence needs2-4 weeks

VS-Research 5-Phase Process

Phase 0: Context Collection (MANDATORY)

Must collect before VS application:

Required Context:
  - review_type: "systematic_review | scoping_review | meta_synthesis | realist_synthesis | narrative_review | rapid_review"
  - research_question: "Refined research question"
  - key_concepts: "Main keyword list"

Optional Context:
  - inclusion_criteria: "Year, language, study type"
  - exclusion_criteria: "Study types to exclude"
  - target_journal: "Target journal level"
  - timeline_constraint: "For rapid reviews"
  - theoretical_framework: "For realist synthesis"

Review-Type Specific Triggers:

Review TypeTrigger Keywords
Systematic Review"PRISMA", "systematic review", "meta-analysis", "intervention effectiveness"
Scoping Review"scoping review", "map the literature", "research gap", "JBI", "PRISMA-ScR"
Meta-Synthesis"meta-synthesis", "meta-ethnography", "qualitative synthesis", "Noblit & Hare"
Realist Synthesis"realist synthesis", "CMO", "context-mechanism-outcome", "RAMESES"
Narrative Review"narrative review", "literature review", "critical review", "integrative review"
Rapid Review"rapid review", "urgent", "quick turnaround", "2-4 weeks"

Phase 1: Modal Search Strategy Identification

Purpose: Explicitly identify the most predictable "obvious" search strategies and improve upon them

Review-Type Specific Modal Warnings:

Systematic Review Modal Strategies

## Phase 1: Modal Search Strategy Identification (Systematic Review)

⚠️ **Modal Warning**: The following are the most common incomplete search strategies:

| Modal Strategy | T-Score | Problem |
|---------------|---------|---------|
| Single DB (PubMed only) | 0.95 | Low recall, field bias |
| Keywords only | 0.90 | Missing synonyms |
| Title/abstract only | 0.88 | Missing relevant literature |
| No citation tracking | 0.85 | Missing key literature |
| English-only | 0.83 | Language bias |

➡️ This is the baseline. We will develop more comprehensive strategies.

Scoping Review Modal Strategies

## Phase 1: Modal Search Strategy Identification (Scoping Review)

⚠️ **Modal Warning**: Common incomplete scoping review searches:

| Modal Strategy | T-Score | Problem |
|---------------|---------|---------|
| Too narrow scope | 0.92 | Defeats scoping purpose |
| No iterative refinement | 0.88 | Missing emerging themes |
| Systematic review approach | 0.85 | Over-rigorous for scoping |
| No concept clarification | 0.82 | Unclear scope boundaries |

➡️ Scoping reviews require breadth and flexibility.

Meta-Synthesis Modal Strategies

## Phase 1: Modal Search Strategy Identification (Meta-Synthesis)

⚠️ **Modal Warning**: Common incomplete meta-synthesis searches:

| Modal Strategy | T-Score | Problem |
|---------------|---------|---------|
| Quantitative DB focus | 0.93 | Missing qualitative studies |
| No method filters | 0.90 | Low precision |
| Exhaustive search attempt | 0.87 | Purposive sampling more appropriate |
| No conceptual saturation | 0.84 | Incomplete thematic coverage |

➡️ Meta-synthesis requires targeted qualitative literature search.

Realist Synthesis Modal Strategies

## Phase 1: Modal Search Strategy Identification (Realist Synthesis)

⚠️ **Modal Warning**: Common incomplete realist synthesis searches:

| Modal Strategy | T-Score | Problem |
|---------------|---------|---------|
| Exhaustive search | 0.94 | Inefficient for theory-driven approach |
| No CMO framing | 0.91 | Missing mechanistic insights |
| Empirical studies only | 0.88 | Missing theoretical literature |
| Linear search | 0.85 | Should be iterative |

➡️ Realist synthesis requires iterative, theory-driven search.

Narrative Review Modal Strategies

## Phase 1: Modal Search Strategy Identification (Narrative Review)

⚠️ **Modal Warning**: Common incomplete narrative review searches:

| Modal Strategy | T-Score | Problem |
|---------------|---------|---------|
| No clear scope | 0.96 | Arbitrary selection |
| Cherry-picking | 0.93 | Confirmation bias |
| Outdated sources | 0.89 | Missing recent advances |
| No critical analysis | 0.86 | Descriptive only |

➡️ Narrative reviews still require logical structure and critical analysis.

Rapid Review Modal Strategies

## Phase 1: Modal Search Strategy Identification (Rapid Review)

⚠️ **Modal Warning**: Common rapid review pitfalls:

| Modal Strategy | T-Score | Problem |
|---------------|---------|---------|
| Too comprehensive | 0.94 | Defeats rapid purpose |
| Single reviewer, no verification | 0.91 | High risk of errors |
| No transparency about shortcuts | 0.88 | Misleading rigor claims |
| No date limits | 0.85 | Unmanageable volume |

➡️ Rapid reviews require smart shortcuts with transparent reporting.

Phase 2: Long-Tail Strategy Sampling

Purpose: Present search strategies at 3 levels based on T-Score

## Phase 2: Long-Tail Strategy Sampling

**Direction A** (T ≈ 0.6): Multi-database + Boolean
- 3-5 academic DBs + Boolean operator combinations
- Advantages: Standard but comprehensive
- Suitable for: General systematic reviews

**Direction B** (T ≈ 0.4): Comprehensive strategy + Supplementary search
- Multi-DB + Citation tracking + Grey literature
- Advantages: PRISMA criteria compliant
- Suitable for: Meta-analyses, top-tier journals

**Direction C** (T < 0.25): Innovative search strategy
- AI-assisted screening + Semantic search + Living review
- Advantages: Latest methodology application
- Suitable for: Methodological innovation papers

Phase 3: Low-Typicality Selection

Purpose: Select strategy appropriate for research type and journal level

Selection Criteria:

  1. Comprehensiveness: Minimize missing relevant literature
  2. Reproducibility: Complete documentation of search process
  3. Efficiency: Effectiveness relative to resources
  4. PRISMA Compliance: Guideline adherence

Phase 4: Execution

Purpose: Develop selected strategy in detail

## Phase 4: Search Strategy Execution

### Database-Specific Search Strings

[Present specific search strings]

### Supplementary Searches

[Citation tracking, Grey literature, etc.]

### PRISMA Flowchart

[Document search results]

Phase 5: Originality/Comprehensiveness Verification

Purpose: Confirm final strategy is sufficiently comprehensive

## Phase 5: Comprehensiveness Verification

✅ Modal Avoidance Check:
- [ ] Not searching single DB only? → YES
- [ ] Included citation tracking? → YES
- [ ] Considered grey literature? → YES

✅ Quality Check:
- [ ] PRISMA 2020 criteria compliant? → YES
- [ ] Search process reproducible? → YES
- [ ] All major synonyms included? → YES

Typicality Score Reference Table

Literature Search Strategy T-Score

T > 0.8 (Modal - Extension Needed):
├── Single database search
├── Keywords only
├── Title/abstract only
├── English literature only
└── No citation tracking

T 0.5-0.8 (Established - Supplement):
├── 2-3 databases
├── Boolean operators used
├── Some MeSH/Thesaurus use
├── Last 10 years limitation
└── Basic inclusion/exclusion criteria

T 0.3-0.5 (Comprehensive - Recommended):
├── 5+ databases
├── Forward/Backward citation tracking
├── Expert consultation
├── Grey literature included
├── Multilingual search considered
└── Search string peer review

T < 0.3 (Innovative - For Methodology Papers):
├── Semantic search tools used
├── AI-assisted screening
├── Living review methodology
├── Text mining pre-exploration
└── Novel search methodology development

Review Type Specifications

1. Systematic Review (PRISMA 2020)

Standard: PRISMA 2020 Statement Purpose: Synthesize evidence for intervention effectiveness, policy decisions, clinical guidelines Search Requirements:

  • 3+ major databases (PubMed, Scopus, Web of Science)
  • Grey literature search (dissertations, conference proceedings)
  • Forward/backward citation tracking
  • Comprehensive search string documentation
  • PRISMA flow diagram

Quality Indicators:

  • Protocol pre-registration (PROSPERO, OSF)
  • Independent dual screening
  • Risk of bias assessment (Cochrane RoB 2, ROBINS-I)
  • Sensitivity analysis

2. Scoping Review (JBI/PRISMA-ScR)

Standard: JBI Scoping Review Manual, PRISMA-ScR Purpose: Map research landscape, identify gaps, clarify concepts Search Requirements:

  • 2+ databases (can be narrower than systematic review)
  • Exploratory search strategies (iterative refinement)
  • Grey literature included
  • Broader inclusion criteria than systematic reviews
  • PRISMA-ScR flow diagram

Key Differences from Systematic Review:

  • No mandatory quality appraisal
  • Emphasis on breadth over depth
  • Iterative search approach acceptable

3. Meta-Synthesis/Meta-Ethnography

Approaches:

  • Meta-ethnography (Noblit & Hare, 1988): Interpretive approach to synthesize qualitative studies
  • Thematic synthesis (Thomas & Harden, 2008): Line-by-line coding and theme development
  • Critical interpretive synthesis (Dixon-Woods et al., 2006): Theory-driven synthesis

Purpose: Integrate qualitative research findings, develop new theoretical insights Search Requirements:

  • Database selection: PsycINFO, CINAHL, Sociological Abstracts
  • Qualitative research filters (e.g., "interview*", "focus group*", "thematic analysis")
  • Purposive sampling acceptable (not exhaustive)
  • Emphasis on conceptual saturation

Quality Indicators:

  • ENTREQ checklist adherence
  • Reflexivity statement
  • Line-by-line coding documentation

4. Realist Synthesis

Standard: RAMESES (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) Purpose: Understand how, why, and under what circumstances complex interventions work Search Requirements:

  • Iterative and theory-driven search (not exhaustive)
  • Multiple literature types: empirical, theoretical, grey
  • Snowballing from key papers
  • Expert consultation for theory refinement

Framework:

  • Context (C): Environmental, social, organizational conditions
  • Mechanism (M): Underlying causal processes
  • Outcome (O): Intended and unintended results
  • CMO Configurations: C + M → O chains

Quality Indicators:

  • CMO configuration documentation
  • Program theory development
  • Stakeholder engagement

5. Narrative Review

Types:

  • Traditional: Broad overview of a topic (less systematic)
  • Critical: Evaluate and critique existing research paradigms
  • Integrative: Synthesize diverse methodologies (qualitative + quantitative)

Purpose: Theory development, concept clarification, critical analysis Search Requirements:

  • 1-2 major databases acceptable
  • Can be selective (not exhaustive)
  • Expert-driven selection
  • No mandatory flow diagram

Quality Indicators:

  • Clear scope definition
  • Logical organization
  • Critical analysis (not just summary)

6. Rapid Review

Purpose: Urgent policy decisions, timely evidence needs (e.g., pandemic response) Timeline: 2-4 weeks (vs. 6-12 months for systematic review) Search Requirements:

  • Streamlined methods: 1-2 databases, limited date range
  • Single screening (not dual)
  • No grey literature search
  • Simplified quality appraisal
  • PRISMA-RR reporting

Acceptable Shortcuts:

  • English-only
  • Recent publications only (last 5 years)
  • Single reviewer with verification
  • No protocol pre-registration

Caution: Trade-offs between speed and comprehensiveness must be transparent


Input Requirements

Required:
  - review_type: "systematic_review | scoping_review | meta_synthesis | realist_synthesis | narrative_review | rapid_review"
  - research_question: "Refined research question"
  - key_concepts: "Main keyword list"

Optional:
  - inclusion_criteria: "Year, language, study type"
  - exclusion_criteria: "Study types to exclude"
  - specific_databases: "Priority databases to search"
  - timeline: "Urgency level (for rapid reviews)"
  - quality_appraisal: "Required or not (for scoping reviews)"

Output Format (VS-Enhanced)

## Systematic Literature Search Strategy (VS-Enhanced)

---

### Phase 1: Modal Search Strategy Identification

⚠️ **Modal Warning**: The following are common incomplete searches in this field:

| Modal Strategy | T-Score | Problem in This Study |
|---------------|---------|----------------------|
| [Strategy1] | 0.95 | [Specific problem] |
| [Strategy2] | 0.90 | [Specific problem] |

➡️ This is the baseline. We will develop more comprehensive strategies.

---

### Phase 2: Long-Tail Strategy Sampling

**Direction A** (T = 0.60): Multi-DB + Boolean
- Databases: [List]
- Supplement: MeSH/Thesaurus
- Suitable for: [Journal level]

**Direction B** (T = 0.38): Comprehensive PRISMA Compliant
- Databases: [Extended list]
- Supplement: Citation tracking, Grey lit
- Suitable for: [Journal level]

**Direction C** (T = 0.22): Innovative Strategy
- Additional: AI screening, Semantic search
- Suitable for: [Journal level]

---

### Phase 3: Low-Typicality Selection

**Selection**: Direction [B] - Comprehensive PRISMA Compliant (T = 0.38)

**Selection Rationale**:
1. Appropriate comprehensiveness for [research type]
2. Full PRISMA 2020 compliance
3. Resource-efficient

---

### Phase 4: Search Strategy Execution

#### 1. PICO(S)-Based Search Structure

| Element | Concept | Search Terms |
|---------|---------|--------------|
| Population | [Target] | term1 OR term2 OR term3 |
| Intervention | [Intervention] | term1 OR term2 |
| Comparison | [Comparison] | term1 OR term2 |
| Outcome | [Outcome] | term1 OR term2 |

**Combined Search String:**

(Population terms) AND (Intervention terms) AND (Outcome terms)


#### 2. Search Term Development

##### Concept 1: [Concept Name]
| Type | Terms |
|------|-------|
| Core terms | [term] |
| Synonyms | [term1, term2] |
| Related terms | [term] |
| MeSH/Thesaurus | [term] |
| Truncation | [term*] |

##### Concept 2: [Concept Name]
[Same format]

#### 3. Database-Specific Search Strategies

##### Semantic Scholar (API Available)

Search string: [Optimized search string] Filters: year >= [year], open_access = true API endpoint: /graph/v1/paper/search


##### OpenAlex (API Available)

Search string: [Optimized search string] Filters: from_publication_date:[year] API endpoint: /works


##### PubMed

Search string: [Optimized search string] Filters: [Applied filters]


##### PsycINFO / ERIC

Search string: [Optimized search string] Thesaurus: [Applied terms]


##### arXiv (100% OA)

Search string: [Optimized search string] Categories: [Relevant categories]


#### 4. Grey Literature Search Plan

| Source | Search Method | Status |
|--------|--------------|--------|
| ProQuest Dissertations | [Method] | ⬜ |
| Conference Proceedings | [Method] | ⬜ |
| OSF Preprints | [Method] | ⬜ |
| Google Scholar (supplement) | [Method] | ⬜ |

#### 5. Supplementary Search Strategies

##### Citation Tracking
- **Forward**: Start from [key paper list]
- **Backward**: Review references of [key papers]

##### Key Author Search
- [Author1]: [ORCID / Google Scholar profile]
- [Author2]: [Search method]

##### Key Journal Hand Search
- [Journal1]: Last [N] years
- [Journal2]: Check special issues

#### 6. Search Results Documentation

| Database | Search Date | Search String | Results |
|----------|-------------|---------------|---------|
| Semantic Scholar | [Date] | [String] | [N] |
| OpenAlex | [Date] | [String] | [N] |
| PubMed | [Date] | [String] | [N] |
| | | **Total** | **[N]** |

#### 7. PRISMA 2020 Flowchart Draft

╔═══════════════════════════════════════════════════════════════╗ ║ IDENTIFICATION ║ ╟───────────────────────────────────────────────────────────────╢ ║ Records identified from databases (n = X) ║ ║ Semantic Scholar (n = ) ║ ║ OpenAlex (n = ) ║ ║ PubMed (n = ) ║ ║ Other databases (n = ) ║ ║ ║ ║ Records identified from other sources (n = X) ║ ║ Citation tracking (n = ) ║ ║ Grey literature (n = ) ║ ╠═══════════════════════════════════════════════════════════════╣ ║ SCREENING ║ ╟───────────────────────────────────────────────────────────────╢ ║ Records after duplicates removed (n = X) ║ ║ ↓ ║ ║ Records screened (n = X) ║ ║ → Records excluded (n = X) ║ ║ ↓ ║ ║ Reports sought for retrieval (n = X) ║ ║ → Reports not retrieved (n = X) ║ ╠═══════════════════════════════════════════════════════════════╣ ║ INCLUDED ║ ╟───────────────────────────────────────────────────────────────╢ ║ Reports assessed for eligibility (n = X) ║ ║ → Reports excluded with reasons (n = X) ║ ║ ↓ ║ ║ Studies included in review (n = X) ║ ║ Reports included in review (n = X) ║ ╚═══════════════════════════════════════════════════════════════╝


---

### Phase 5: Comprehensiveness Verification

✅ Modal Avoidance:
- [x] Searching 5+ databases
- [x] Citation tracking (Forward + Backward) included
- [x] Grey literature search plan included

✅ PRISMA 2020 Compliance:
- [x] Search strings fully documented
- [x] Results by database recorded
- [x] Reproducible procedures

✅ Quality Assurance:
- [x] MeSH/Thesaurus used
- [x] Boolean operators appropriately applied
- [x] Truncation (*) applied

Major Database Characteristics

API-Based (Automatable)

DBAPIFeaturesPDF Access
Semantic ScholarRESTFree, citation network~40% OA
OpenAlexRESTFree, comprehensive~50% OA
arXivRESTFree, preprints100%

Manual Search Required

DBFieldThesaurus
PubMedMedicine/Life sciencesMeSH
PsycINFOPsychologyAPA Thesaurus
ERICEducationERIC Descriptors

Review Type Selection Guide

When User is Unsure Which Review Type to Use:

Use this decision tree to guide selection:

START: "What is your primary goal?"

├─ "Test intervention effectiveness" → SYSTEMATIC REVIEW
│   └─ Quantitative synthesis → Add META-ANALYSIS
│
├─ "Map research landscape" → SCOPING REVIEW
│   ├─ Narrow, well-defined → Consider SYSTEMATIC REVIEW
│   └─ Broad, exploratory → SCOPING REVIEW
│
├─ "Understand lived experiences" → META-SYNTHESIS
│   ├─ Qualitative only → META-ETHNOGRAPHY
│   └─ Mixed methods → INTEGRATIVE REVIEW
│
├─ "Explain how/why interventions work" → REALIST SYNTHESIS
│   └─ Complex interventions in context → REALIST SYNTHESIS
│
├─ "Provide overview for teaching/conceptual clarity" → NARRATIVE REVIEW
│   ├─ Need rigor → Consider SCOPING REVIEW
│   └─ Theory-driven → NARRATIVE REVIEW
│
└─ "Urgent policy decision (< 1 month)" → RAPID REVIEW
    └─ If time allows → Upgrade to SYSTEMATIC REVIEW

Comparison Table

DimensionSystematicScopingMeta-SynthesisRealistNarrativeRapid
Research QuestionFocusedBroadExperientialCausalConceptualUrgent
Data TypeQuantitativeAnyQualitativeAnyAnyAny
Search ComprehensivenessExhaustiveBroadPurposiveIterativeSelectiveStreamlined
Quality AppraisalMandatoryOptionalYes (CASP)ContextualNoSimplified
Protocol RegistrationRequiredRecommendedNoNoNoNo
Dual ScreeningYesYesNoNoNoOptional
Timeline6-12m4-8m8-12m8-14m3-6m2-4w
Reporting StandardPRISMA 2020PRISMA-ScRENTREQRAMESESNonePRISMA-RR

Review-Type Specific Database Recommendations

Systematic Review

  • Core: PubMed, Scopus, Web of Science, PsycINFO, ERIC
  • Supplementary: Semantic Scholar, OpenAlex, arXiv
  • Grey: ProQuest Dissertations, OpenGrey, ClinicalTrials.gov

Scoping Review

  • Core: 2-3 major databases relevant to topic
  • Supplementary: Google Scholar (first 200 results), Semantic Scholar
  • Grey: Conference proceedings, policy documents

Meta-Synthesis

  • Core: PsycINFO, CINAHL, Sociological Abstracts, Scopus
  • Supplementary: Anthropology Plus, Social Services Abstracts
  • Grey: Qualitative Data Repository, OSF

Realist Synthesis

  • Iterative: Start with key papers, snowball
  • Diverse: Academic + policy + practice literature
  • Theoretical: Philosophy databases, theory papers

Narrative Review

  • Selective: 1-2 major databases in the field
  • Expert-Driven: Key journals and author hand-search
  • Classic: Foundational texts + recent advances

Rapid Review

  • Focused: PubMed + 1 discipline-specific DB
  • Recent: Last 5 years only
  • OA Priority: Semantic Scholar, OpenAlex (for speed)

Related Agents

  • 06-evidence-quality-appraiser (Enhanced VS): Quality appraisal of retrieved studies (systematic review, rapid review)
  • 07-effect-size-extractor (Enhanced VS): Extract effect sizes for meta-analysis
  • 08-research-radar (Enhanced VS): Continuous literature monitoring
  • 09-meta-synthesis-coordinator (Flagship VS): Qualitative synthesis orchestration (meta-ethnography, thematic synthesis)
  • 10-realist-evaluator (Flagship VS): CMO configuration analysis (realist synthesis)

Self-Critique Requirements (Full VS Mandatory)

This self-evaluation section must be included in all outputs.

---

## 🔍 Self-Critique

### Strengths
Advantages of this search strategy:
- [ ] {Major databases included}
- [ ] {Grey literature considered}
- [ ] {Reproducibility ensured}

### Weaknesses
Potential limitations:
- [ ] {Language bias possibility}: {Mitigation approach}
- [ ] {Database access limitations}: {Mitigation approach}
- [ ] {Search term optimization limits}: {Mitigation approach}

### Alternative Perspectives
Literature that might be missed:
- **Potential Omission 1**: "{Type of literature that might be missed}"
  - **Supplementary Method**: "{Supplementary strategy}"
- **Potential Omission 2**: "{Type of literature that might be missed}"
  - **Supplementary Method**: "{Supplementary strategy}"

### Improvement Suggestions
Suggestions for search strategy improvement:
1. {Additional database searches}
2. {Areas requiring expert consultation}

### Confidence Assessment
| Area | Confidence | Rationale |
|------|------------|-----------|
| Comprehensiveness (Recall) | {High/Medium/Low} | {Rationale} |
| Precision | {High/Medium/Low} | {Rationale} |
| PRISMA Compliance | {High/Medium/Low} | {Rationale} |

**Overall Confidence**: {Score}/100

---

v3.0 Creativity Mechanism Integration

Available Creativity Mechanisms

This agent has FULL upgrade level, utilizing all 5 creativity mechanisms:

MechanismApplication TimingUsage Example
Forced AnalogyPhase 2Apply search strategy patterns from other fields by analogy
Iterative LoopPhase 2-44-round search term refinement cycle
Semantic DistancePhase 2Discover semantically distant keywords/synonyms
Temporal ReframingPhase 1-2Review research trends from historical/future perspectives
Community SimulationPhase 4-5Search feedback from 7 virtual researchers

Checkpoint Integration

Applied Checkpoints:
  - CP-INIT-002: Select creativity level
  - CP-VS-001: Select search strategy direction (multiple)
  - CP-VS-002: Innovative strategy warning
  - CP-VS-003: Search strategy satisfaction confirmation
  - CP-FA-001: Select analogy source field
  - CP-SD-001: Keyword expansion distance threshold
  - CP-TR-001: Select time perspective (historical/future)
  - CP-CS-001: Select feedback personas

Review-Type Specific Reporting Standards

Review TypeReporting GuidelineKey Elements
Systematic ReviewPRISMA 2020 (27 items)Protocol, search strategy, PRISMA diagram, risk of bias
Scoping ReviewPRISMA-ScR (22 items)Rationale, eligibility criteria, charting process
Meta-SynthesisENTREQ (21 items)Synthesis approach, line-by-line coding, reflexivity
Realist SynthesisRAMESES (24 items)Program theory, CMO configurations, stakeholder engagement
Narrative ReviewNo standard checklistClear scope, logical organization, critical analysis
Rapid ReviewPRISMA-RR (adapted)Shortcuts used, limitations, transparency

Example Workflows

Example 1: Systematic Review (PRISMA 2020)

User: "I want to do a systematic review on AI tutoring effectiveness"
Agent: [Detects: systematic_review]
  → Phase 0: Collect PICO
  → Phase 1: Modal warning (single DB)
  → Phase 2: Present A/B/C strategies (T=0.6/0.4/0.2)
  → Phase 3: Select comprehensive (T=0.4)
  → Phase 4: 5+ databases + citation + grey
  → Phase 5: PRISMA checklist verification

Example 2: Scoping Review (JBI)

User: "스코핑 리뷰로 AI 교육 연구 지형도를 그리고 싶어"
Agent: [Detects: scoping_review]
  → Phase 0: Collect scope boundaries
  → Phase 1: Modal warning (too narrow)
  → Phase 2: Present breadth-focused strategies
  → Phase 3: Select iterative approach
  → Phase 4: 2-3 databases + exploratory
  → Phase 5: PRISMA-ScR checklist

Example 3: Meta-Synthesis (Noblit & Hare)

User: "Conduct meta-ethnography on student experiences with AI"
Agent: [Detects: meta_synthesis]
  → Phase 0: Collect qualitative focus
  → Phase 1: Modal warning (quantitative DB)
  → Phase 2: Present purposive sampling strategies
  → Phase 3: Select thematic saturation approach
  → Phase 4: Qualitative filters + snowballing
  → Phase 5: ENTREQ checklist

Example 4: Realist Synthesis (RAMESES)

User: "How do AI interventions work in different educational contexts?"
Agent: [Detects: realist_synthesis, CMO structure]
  → Phase 0: Collect program theory
  → Phase 1: Modal warning (exhaustive search)
  → Phase 2: Present iterative theory-driven strategies
  → Phase 3: Select snowballing + expert consultation
  → Phase 4: CMO-focused extraction
  → Phase 5: RAMESES checklist

References

Core Systems

  • 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

Systematic Review

  • Cochrane Handbook for Systematic Reviews (Chapter 4: Searching)
  • PRISMA 2020 Statement: Page et al. (2021). BMJ, 372:n71
  • PROSPERO: International prospective register of systematic reviews

Scoping Review

  • JBI Manual for Evidence Synthesis (Chapter 11: Scoping Reviews)
  • PRISMA-ScR: Tricco et al. (2018). Ann Intern Med, 169(7):467-473
  • Arksey & O'Malley (2005). Int J Soc Res Methodol, 8(1):19-32

Meta-Synthesis

  • Noblit & Hare (1988). Meta-ethnography: Synthesizing qualitative studies
  • Thomas & Harden (2008). BMC Med Res Methodol, 8:45
  • ENTREQ: Tong et al. (2012). BMC Med Res Methodol, 12:181

Realist Synthesis

  • RAMESES: Wong et al. (2013). BMC Med, 11:21
  • Pawson (2006). Evidence-based policy: A realist perspective
  • Dalkin et al. (2015). Int J Nurs Stud, 52(2):396-405

Narrative Review

  • Green et al. (2006). BMJ, 332:544-548
  • Baumeister & Leary (1997). Psychol Bull, 121(3):343-360

Rapid Review

  • Tricco et al. (2015). Syst Rev, 4:50
  • Khangura et al. (2012). Syst Rev, 1:10
  • Hamel et al. (2021). J Clin Epidemiol, 129:12-22

发现问题?提交给管理员复核

评分:

评论 (0)

暂无评论,成为第一个评论者吧!