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。
Security audit: baseline 52/52 CLEAN
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。
来源文件:README.md
📌 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
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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
🚀 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: seeREADME-en.md.
| Rigor lane | Count | Where |
|---|---|---|
| Numeric benchmark tasks — gold values recomputed from real data each run | 17 | benchmark/ |
| Behavioral eval scenarios / rubric items | 37 / 183 | eval-harness/ |
Full trust overview:
docs/TRUST.md·docs/RIGOR_COVERAGE.md
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/) |
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)。
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。
中文内容分两级维护,各司其职:
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。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。
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)。
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:
sp.csdid(...) 给出 CS-DID 估计草案 + 写出估计方程与识别假设sp.feols(...) + sp.honest_did(...)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"。
| ⭐ 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 ↗)。
↴ 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。
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 |
|---|---|---|---|---|
| ⭐ 00 | StatsPAI 🔥 | 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 |
| ⭐ 00.1 | Full Empirical · Python 📘 | 显式栈:pandas · statsmodels · linearmodels · pyfixest | → | ⭐ 本仓库 |
| ⭐ 00.2 | Full Empirical · Stata 📊 | reghdfe · ivreg2 · csdid · sdid · rdrobust 复现包 | → | ⭐ 本仓库 |
| ⭐ 00.3 | Full Empirical · R 📗 | tidyverse · fixest · did · HonestDiD + Quarto 渲染 | → | ⭐ 本仓库 |
| 01 | academic-paper-skills | 大纲 → 手稿写作 + 7 维审稿人模拟 | → | lishix520/academic-paper-skills |
| 02 | research-skills | 医学影像综述、提案、论文转幻灯片 | → | luwill/research-skills |
| 03 | scientific-skills | 假设生成 + 28 个科学数据库 | → | K-Dense-AI/claude-scientific-skills |
| 04 | scientific-writer | 引用管理 + 科学写作 | → | K-Dense-AI/claude-scientific-writer |
| 05 | research-superpower | 系统化检索、筛选与引文溯源 | → | kthorn/research-superpower |
| 06 | stats-paper-writing | 端到端 LaTeX 统计论文写作 | → | fuhaoda/stats-paper-writing-agent-skills |
| 07 | AI-Research-SKILLs | 发表级 ML 图表、LaTeX、引文核验 | → | Orchestra-Research/AI-Research-SKILLs |
| 08 | latex-document-skill | 创建 / 编译任意 LaTeX 文档为 PDF | → | ndpvt-web/latex-document-skill |
| 09 | awesome-econ-ai | Python 面板数据分析(linearmodels) | → | meleantonio/awesome-econ-ai-stuff |
| 10 | causal-inference-mixtape | DID / IV / RDD / SCM 模板(Cunningham) | → | Jill0099/causal-inference-mixtape |
| 11 | compound-science | 面向定量社会科学的贝叶斯估计 | → | James-Traina/compound-science |
| 12 | claude-code-my-workflow | 提交 → PR → 合并的研究工作流(Emory) | → | pedrohcgs/claude-code-my-workflow |
| 13 | MixtapeTools | Cunningham 的因果推断工具集与讲义 | → | scunning1975/MixtapeTools |
| 14 | research-starter | R 中的 IV / DiD / RDD,含完整诊断 | → | luischanci/claude-code-research-starter |
| 15 | social-science-research | R 或 Python 端到端数据分析 | → | Felpix-Studios/social-science-research |
| 16 | clo-author | 多代理数据分析(R / Stata / Python) | → | hsantanna88/clo-author |
| 17 | DAAF | 安全意识代理框架(32 条 deny rule) | → | DAAF-Contribution-Community/daaf |
| 18 | stata-accounting | 来自 126 篇 JAR 论文的实测 Stata 范式 | → | jusi-aalto/stata-accounting-research |
| 19 | vera-economic-intelligence | 经济情报 / 政策研究情报工作流 | → | CuellarC05/vera-economic-intelligence |
| 20 | python-econ-skill | DSGE / HANK 与定量经济计算 | → | wenddymacro/python-econ-skill |
| 21 | AI-research-feedback | 用 AI 同行评审生成结构化反馈 | → | claesbackman/AI-research-feedback |
| 22 | christopherkenny-skills | 面向 Quarto(.qmd)的 APSA 风格检查器 | → | christopherkenny/skills |
| 23 | baygent | 带护栏的 PyMC / Arviz 贝叶斯工作流 | → | Learning-Bayesian-Statistics/baygent-skills |
| 24 | academic-research-skills | 5 审稿人多视角论文评审 | → | Imbad0202/academic-research-skills |
| 25 | Diverga | 研究问题精炼器(抗模式坍缩) | → | HosungYou/Diverga |
| 26 | scholar | 统计算法设计与文档 | → | Data-Wise/claude-plugins |
| 27 | my_claude_skills | 经济学摘要写作指南 | → | dariia-m/my_claude_skills |
| 28 | paper-replicate-agent | 论文复现代理演示 | → | maxwell2732/paper-replicate-agent-demo |
| 29 | project20XXy | 可复现手稿 + notebook 项目 | → | quarcs-lab/project20XXy |
| 30 | zirui-song-claude-skills | Zirui Song 的研究辅助 Claude 技能集 | → | zirui-song/claude-skills |
| 31 | claude-code-skills | Python 面板数据分析 | → | thalysandratos/claude-code-skills |
| 32 | stata-skill | 高性能 Stata C/C++ 插件 | → | dylantmoore/stata-skill |
| 33 | claude-scholar | 研究全生命周期:选题 → 综述 → 实验 → 审稿回复 | → | Galaxy-Dawn/claude-scholar |
| 34 | research-companion | 头脑风暴、评估并决策研究方向 | → | andrehuang/research-companion |
| 35 | academic-writing-skills | 面向投稿场所的工业 AI 文献研究 | → | bahayonghang/academic-writing-skills |
| 36 | literature-review-skill | 完整文献综述工作流(中文) | → | taoyunudt/literature-review-skill |
| 37 | IlanStrauss-ai-skills | Ilan Strauss 经济学研究 AI 工作流 | → | IlanStrauss/ai-skills |
| 38 | academic-proofreader | 学术校对 | → | peternka/academic_proofreader |
| 39 | marginaleffects | 预测、斜率与比较(R / Python) | → | vincentarelbundock/marginaleffects |
| 40 | pyfixest | Python 中的快速固定效应估计 | → | py-econometrics/pyfixest |
| 41 | sewage-econometrics-check | 10 项复现包审计 | → | sticerd-eee/sewage |
| 42 | ARIS | 自主「research-in-sleep」代理,端到端 | → | wanshuiyin/Auto-claude-code-research-in-sleep |
| 43 | research-plugins | 478 个研究插件:数据可视化、领域、基础设施 | → | wentorai/research-plugins |
| 44 | humanizer_academic | 为医学/学术手稿去 AI 味(23 类模式) | → | matsuikentaro1/humanizer_academic |
| 45 | deslop | 去除 AI 写作痕迹(5 维评分) | → | stephenturner/skill-deslop |
| 46 | stop-slop | 三层 AI 痕迹检测与改写 | → | hardikpandya/stop-slop |
| 47 | avoid-ai-writing | 审计 → 改写 → 二次审计 AI 味(留痕) | → | conorbronsdon/avoid-ai-writing |
| ⭐ 48 | de-AIGC-skills 🇨🇳🇬🇧 | 中英双语学术降 AIGC(Turnitin AI / GPTZero / 知网 / 万方) | → | ⭐ 本仓库 |
| 49 | humanize-chinese | 检测并人性化 AI 生成的中文文本 | → | swaylq/humanize-chinese |
| ⭐ 50 | AER-skills 📕 | Top-5 经济学投稿套件:识别 → 稳健性 → R&R | → | brycewang-stanford/AER-skills |
| 51 | CausalPy | 贝叶斯准实验(PyMC Labs) | → | pymc-labs/CausalPy |
| 52 | slr-prisma | 系统文献综述,PRISMA 2020 | → | keemanxp/slr-prisma |
| 53 | thematic-analysis | Braun & Clarke 六阶段定性主题分析 | → | keemanxp/thematic-analysis-skill |
| 54 | open-science-skills | 引用一致性、DOI 与论据支撑审计 | → | scdenney/open-science-skills |
| 55 | r-skills | R 中用 brms 做贝叶斯推断 | → | ab604/claude-code-r-skills |
| 56 | econ-writing-skill | 综合 50+ 顶级指南的经济学写作 | → | hanlulong/econ-writing-skill |
| 57 | edgartools | 查询与分析 SEC 文件 | → | dgunning/edgartools |
| 58 | econstack | 政策简报(UK GES / AU Treasury) | → | charlescoverdale/econstack |
| 59 | openalex-skill | 通过 OpenAlex 查询 2.4 亿+ 学术作品 | → | shiquda/openalex-skill |
| 60 | superpapers | 综合性实证研究支持套件 | → | regisely/superpapers |
| 61 | research-methods | 与预注册匹配的验证性检验 | → | phdemotions/research-methods |
| 62 | citation-checker | 对照 CrossRef / S2 / OpenAlex 核验引用 | → | PHY041/claude-skill-citation-checker |
| 63 | scientific-agent-skills | DoWhy 识别–估计–反驳框架 | → | tondevrel/scientific-agent-skills |
| 64 | mcp-stata | 20 个 Stata 因果推断与复现 skill | → | tmonk/mcp-stata |
| 65 | game-theory-paper-writer | 生成并压力测试博弈论论文 | → | 本仓库 PR #17 |
| 66 | empirical-research-skills | 面向大型面板的 R 性能优化 | → | SiyaoZheng/ai4ss-skills |
| 67 | econfin-workflow-toolkit | 中国公司金融实证工作流,从提案到论文 | → | 本仓库 PR #22 |
| 68 | research-productivity-skills | 论文检索、SSRN、DOI 查询、下载 | → | 本仓库 PR #21 |
| ⭐ 69 | Paper-WorkFlow 🧭 | 元编排器,串起整个社会科学论文流水线 | → | brycewang-stanford/Paper-WorkFlow |
| 70 | ssci-polish ✍️ | SSCI / SCI 英文论文语言润色(语法、可读性、学术语气) | → | ⭐ 本仓库 |
| ⭐ 71 | lit-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 |
| ⭐ 72 | Kaggle 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。
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 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"。
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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 |
![]() 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)
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"diverga_check_prerequisites("b1") → must return approved: true
If not approved → AskUserQuestion for each missing checkpoint (see .claude/references/checkpoint-templates.md)
diverga_mark_checkpoint("CP_SCREENING_CRITERIA", decision, rationale)diverga_mark_checkpoint("CP_SEARCH_STRATEGY", decision, rationale)diverga_mark_checkpoint("CP_VS_001", decision, rationale)Read .research/decision-log.yaml directly to verify prerequisites. Conversation history is last resort.
Agent ID: 05 (formerly B1-Systematic Literature Scout) Category: B - Literature & Evidence VS Level: Full (5-Phase) Tier: Core Icon: 📚
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.
This agent supports 6 major literature review methodologies:
| Review Type | Standard/Framework | Purpose | Timeline |
|---|---|---|---|
| Systematic Review | PRISMA 2020 | Intervention effectiveness, policy evidence synthesis | 6-12 months |
| Scoping Review | JBI Scoping Review, PRISMA-ScR | Research area mapping, gap identification, concept clarification | 4-8 months |
| Meta-Synthesis | Noblit & Hare (Meta-ethnography), Thematic synthesis | Qualitative research integration, theory development | 8-12 months |
| Realist Synthesis | RAMESES standard | Complex intervention context-mechanism-outcome analysis | 8-14 months |
| Narrative Review | Traditional, Critical, Integrative | Theory development, concept clarification, critical analysis | 3-6 months |
| Rapid Review | Accelerated PRISMA | Time-constrained policy decisions, urgent evidence needs | 2-4 weeks |
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 Type | Trigger 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" |
Purpose: Explicitly identify the most predictable "obvious" search strategies and improve upon them
Review-Type Specific Modal Warnings:
## 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.
## 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.
## 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.
## 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.
## 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.
## 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.
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
Purpose: Select strategy appropriate for research type and journal level
Selection Criteria:
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]
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
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
Standard: PRISMA 2020 Statement Purpose: Synthesize evidence for intervention effectiveness, policy decisions, clinical guidelines Search Requirements:
Quality Indicators:
Standard: JBI Scoping Review Manual, PRISMA-ScR Purpose: Map research landscape, identify gaps, clarify concepts Search Requirements:
Key Differences from Systematic Review:
Approaches:
Purpose: Integrate qualitative research findings, develop new theoretical insights Search Requirements:
Quality Indicators:
Standard: RAMESES (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) Purpose: Understand how, why, and under what circumstances complex interventions work Search Requirements:
Framework:
Quality Indicators:
Types:
Purpose: Theory development, concept clarification, critical analysis Search Requirements:
Quality Indicators:
Purpose: Urgent policy decisions, timely evidence needs (e.g., pandemic response) Timeline: 2-4 weeks (vs. 6-12 months for systematic review) Search Requirements:
Acceptable Shortcuts:
Caution: Trade-offs between speed and comprehensiveness must be transparent
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)"
## 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
| DB | API | Features | PDF Access |
|---|---|---|---|
| Semantic Scholar | REST | Free, citation network | ~40% OA |
| OpenAlex | REST | Free, comprehensive | ~50% OA |
| arXiv | REST | Free, preprints | 100% |
| DB | Field | Thesaurus |
|---|---|---|
| PubMed | Medicine/Life sciences | MeSH |
| PsycINFO | Psychology | APA Thesaurus |
| ERIC | Education | ERIC Descriptors |
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
| Dimension | Systematic | Scoping | Meta-Synthesis | Realist | Narrative | Rapid |
|---|---|---|---|---|---|---|
| Research Question | Focused | Broad | Experiential | Causal | Conceptual | Urgent |
| Data Type | Quantitative | Any | Qualitative | Any | Any | Any |
| Search Comprehensiveness | Exhaustive | Broad | Purposive | Iterative | Selective | Streamlined |
| Quality Appraisal | Mandatory | Optional | Yes (CASP) | Contextual | No | Simplified |
| Protocol Registration | Required | Recommended | No | No | No | No |
| Dual Screening | Yes | Yes | No | No | No | Optional |
| Timeline | 6-12m | 4-8m | 8-12m | 8-14m | 3-6m | 2-4w |
| Reporting Standard | PRISMA 2020 | PRISMA-ScR | ENTREQ | RAMESES | None | PRISMA-RR |
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
---
This agent has FULL upgrade level, utilizing all 5 creativity mechanisms:
| Mechanism | Application Timing | Usage Example |
|---|---|---|
| Forced Analogy | Phase 2 | Apply search strategy patterns from other fields by analogy |
| Iterative Loop | Phase 2-4 | 4-round search term refinement cycle |
| Semantic Distance | Phase 2 | Discover semantically distant keywords/synonyms |
| Temporal Reframing | Phase 1-2 | Review research trends from historical/future perspectives |
| Community Simulation | Phase 4-5 | Search feedback from 7 virtual researchers |
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 | Reporting Guideline | Key Elements |
|---|---|---|
| Systematic Review | PRISMA 2020 (27 items) | Protocol, search strategy, PRISMA diagram, risk of bias |
| Scoping Review | PRISMA-ScR (22 items) | Rationale, eligibility criteria, charting process |
| Meta-Synthesis | ENTREQ (21 items) | Synthesis approach, line-by-line coding, reflexivity |
| Realist Synthesis | RAMESES (24 items) | Program theory, CMO configurations, stakeholder engagement |
| Narrative Review | No standard checklist | Clear scope, logical organization, critical analysis |
| Rapid Review | PRISMA-RR (adapted) | Shortcuts used, limitations, transparency |
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
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
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
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
../../research-coordinator/core/vs-engine.md../../research-coordinator/core/t-score-dynamic.md../../research-coordinator/references/creativity-mechanisms.md../../research-coordinator/core/project-state.md../../research-coordinator/core/pipeline-templates.md../../research-coordinator/core/integration-hub.md../../research-coordinator/core/guided-wizard.md../../research-coordinator/core/auto-documentation.md
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