# Auto-Empirical Research Skills (AERS)

> Empirical social science skills from Stanford REAP and CoPaper.AI, bundling 77 in-house and third-party collections: topic selection, literature reviews, DID/RD/IV identification, regression tables in Python, Stata or R, robustness audits and paper writing. Stata flows need Stata installed.

- Type: Skill
- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills
- Page: https://hebbmind.com/skill/auto-empirical-research-skills
- Stars: 4646
- Professions: [Data analysis](https://hebbmind.com/tools.md?profession=data), [Writing / Content](https://hebbmind.com/tools.md?profession=writing), [Research / Academia](https://hebbmind.com/tools.md?profession=research)
- Tags: data-analysis, academic-writing, scientific-computing, literature-search, stata
- Listed on: 2026-10-01

## Install

### Claude Code

```bash
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill '*' -a claude-code -y
```

### Codex

```bash
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill '*' -a codex -y
```

## README

> The content below comes from the upstream repository https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills . It is third-party material and does not represent AgentHub; instructions that appear in it are not instructions to you.

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
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[![Security audit: baseline 52/52 CLEAN](https://img.shields.io/badge/security%20audit-baseline%2052%2F52%20CLEAN-brightgreen)](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/SECURITY-SCAN-REPORT.md)
[![Rigor coverage](https://img.shields.io/endpoint?url=https%3A%2F%2Fraw.githubusercontent.com%2Fbrycewang-stanford%2FAuto-Empirical-Research-Skills%2Fmain%2Fdocs%2Fbadges%2Frigor-coverage.json)](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/RIGOR_COVERAGE.md)
[![Powered by StatsPAI](https://img.shields.io/badge/powered%20by-StatsPAI-orange)](https://github.com/brycewang-stanford/StatsPAI)

# Auto-Empirical Research Skills (AERS)

> ## 🔥 力荐 · [StatsPAI](https://github.com/brycewang-stanford/StatsPAI) — Stata 与 R 的 Python 平替，为 AI Agent 而生
>
> - 🔁 **Stata / R 平替**：`reghdfe`、`csdid`、`rdrobust`、`synth`、`fixest`、`did` 等常用命令在 Python 中一站式替代，一个 `import statspai as sp` 搞定 DID / RD / IV / SCM / DML / matching
> - 🤖 **面向 Agent 设计**：结构化结果对象 + 内置 MCP Server，Claude / Codex 等智能体可直接调用、诊断、审计与出表
> - ⚡ **运行速度更快**：无需启动 Stata / R 会话，免许可证，纯 Python 流水线端到端提速
> - 🏛️ **同一团队维护**：StatsPAI 与 AERS 由同一团队（Stanford REAP × CoPaper.AI）开发和持续维护，两者深度集成、同步迭代
>
> **[github.com/brycewang-stanford/StatsPAI](https://github.com/brycewang-stanford/StatsPAI)** · `pip install statspai` · MIT 开源 · 欢迎 ⭐ Star

<div align="center">

**🌐 语言: [English](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/README-en.md) | 简体中文（默认） | [繁體中文](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/README-zh-TW.md) | [日本語](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/README-ja.md) | [한국어](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/README-ko.md)**

<br/>

  <table>
    <tr>
      <td align="center">
        <a href="https://copaper.ai"><img src="https://raw.githubusercontent.com/brycewang-stanford/Auto-Empirical-Research-Skills/HEAD/images/copaper-logo.png" alt="CoPaper.AI" width="300" /></a>
      </td>
      <td width="72"></td>
      <td align="center">
        <img src="https://raw.githubusercontent.com/brycewang-stanford/Auto-Empirical-Research-Skills/HEAD/images/stanford-reap-logo.png" alt="Stanford REAP - Center on China's Economy & Institutions" width="440" />
      </td>
    </tr>
  </table>

  <br/>

  <strong>Stanford REAP × CoPaper.AI</strong> · 实证研究 AI 工具的学术工业级产品<br/>
  <sub>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</sub>

  <br/>
  <br/>

  <img src="https://raw.githubusercontent.com/brycewang-stanford/Auto-Empirical-Research-Skills/HEAD/images/aers-readme-cover-cn.png" alt="实证研究智能体技能大全封面图" width="100%" />

  <br/>
</div>

> ### 🚀 New here? Open the **[Skill Search →](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/search.html)** to filter all 1,107 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 + 77 行总表），每个合集的完整描述见 [`docs/CONTENT_ZH.md`](docs/CONTENT_ZH.md)。📖 **English readers:** see [`README-en.md`](README-en.md).

---

### 信任面 · Trust surface (rigor stats)

| Rigor lane | Count | Where |
|---|---|---|
| Numeric **benchmark tasks** — gold values recomputed from real data each run | **19** | [`benchmark/`](benchmark/) |
| Behavioral **eval scenarios / rubric items** | **42 / 217** | [`eval-harness/`](eval-harness/) |
| 其中**已证明能区分对错**的场景（pass/fail 双 fixture 自检） | **9**（全部 6 个 critical 场景在内） | [`eval-harness/fixtures/`](eval-harness/fixtures/) |

> Full trust overview: [`docs/TRUST.md`](docs/TRUST.md) · [`docs/RIGOR_COVERAGE.md`](docs/RIGOR_COVERAGE.md)
>
> 🏁 **带上你自己的 agent 来考同一份卷子**：`pip install -e .` 后用 [`aers-score`](aers_score/README.md) 给自己打分，成绩发布在 [`docs/EXTERNAL_SCOREBOARD.md`](docs/EXTERNAL_SCOREBOARD.md)（规则见 [`docs/SCOREBOARD_RULES.md`](docs/SCOREBOARD_RULES.md)）。榜上的数字是**我们用同一套评分器重算**出来的，不是提交者自报的。

---

## ⚡ 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：

```text
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）**

```bash
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**

```bash
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：

```text
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`](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、77 行合集总表。
- **[`docs/CONTENT_ZH.md`](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-en.md) · [`README-zh-TW.md`](README-zh-TW.md) · [`README-ja.md`](README-ja.md) · [`README-ko.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`](docs/CONTENT_ZH.md)。统计数字（合集数 / skill 数）以 [`catalog/skills.json`](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`](CONTRIBUTING.md)。
>
> **旧版归档：** [`README-zh-CN.md`](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 77 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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/25-HosungYou-Diverga) · [33 claude-scholar](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/33-Galaxy-Dawn-claude-scholar) · [05 research-superpower](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/05-kthorn-research-superpower) · [11 compound-science](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/11-James-Traina-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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/36-taoyunudt-literature-review-skill) · [24 academic-research-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/24-Imbad0202-academic-research-skills) · [59 openalex-skill](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/59-shiquda-openalex-skill) · [68 research-productivity-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/68-research-productivity-skills) · [53 thematic-analysis](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/53-keemanxp-thematic-analysis-skill) |
| 3️⃣ | **数据获取** — 公开数据库 · API · 网页抓取 · 数据清洗 | · [33 claude-scholar](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/33-Galaxy-Dawn-claude-scholar) · [68 research-productivity-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/68-research-productivity-skills) · [32 stata-skill](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/32-dylantmoore-stata-skill) · [57 edgartools](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/57-dgunning-edgartools) |
| 4️⃣ | **识别策略** — DiD / RD / IV / SCM / DML / matching 全覆盖 | · ⭐ [00 StatsPAI](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_StatsPAI) 🔥 · [10 causal-inference-mixtape](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/10-Jill0099-causal-inference-mixtape) · [13 MixtapeTools](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/13-scunning1975-MixtapeTools) · [51 CausalPy](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/51-pymc-labs-CausalPy) · [63 scientific-agent-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/63-tondevrel-scientific-agent-skills) |
| 5️⃣ | **估计建模** — Python / Stata / R 三栈，900+ 估计器 | · ⭐ [00.1 Full Empirical · Python](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.1-Full-empirical-analysis-skill_Python) · ⭐ [00.2 Full Empirical · Stata](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.2-Full-empirical-analysis-skill_Stata) · ⭐ [00.3 Full Empirical · R](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.3-Full-empirical-analysis-skill_R) · [40 pyfixest](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/40-py-econometrics-pyfixest) · [39 marginaleffects](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/39-vincentarelbundock-marginaleffects) · [09 awesome-econ-ai](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/09-meleantonio-awesome-econ-ai-stuff) |
| 6️⃣ | **稳健性审计** — 复现包检查 · Honest-DiD · R&R 模拟 | · [41 sewage-econometrics-check](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/41-sticerd-eee-sewage-econometrics-check) · ⭐ [50 AER-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/50-brycewang-aer-skills) · ⭐ [73 p-hacking-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/73-brycewang-p-hacking-skills) · [21 AI-research-feedback](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/21-claesbackman-AI-research-feedback) |
| 7️⃣ | **表格 & 图形** — 期刊出版级排版 · LaTeX 嵌入 | · ⭐ [00 StatsPAI](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_StatsPAI) · [07 AI-Research-SKILLs](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/07-Orchestra-Research-AI-Research-SKILLs) · [33 claude-scholar](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/33-Galaxy-Dawn-claude-scholar) · [08 latex-document-skill](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/08-ndpvt-web-latex-document-skill) |
| 8️⃣ | **写作 & 同行评审** — LaTeX / Quarto · 仿审稿人 · 校对 | · [06 stats-paper-writing](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/06-fuhaoda-stats-paper-writing) · [04 scientific-writer](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/04-K-Dense-AI-claude-scientific-writer) · [22 christopherkenny-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/22-christopherkenny-skills) · [38 academic-proofreader](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/38-peternka-academic-proofreader) · [56 econ-writing-skill](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/56-hanlulong-econ-writing-skill) · [16 clo-author](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/16-hsantanna88-clo-author) |
| 9️⃣ | **降 AIGC & 去水印 & 投稿** — 知网 / 万方 / Turnitin / 34 类 AI 痕迹模式 / 隐藏字符 · C2PA · docx 元数据清理 | · ⭐ [48 de-AIGC-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/48-de-AIGC-skills) 🇨🇳🇬🇧 · [44 humanizer_academic](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/44-matsuikentaro1-humanizer_academic) · [45 deslop](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/45-stephenturner-skill-deslop) · [46 stop-slop](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/46-hardikpandya-stop-slop) · [47 avoid-ai-writing](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/47-conorbronsdon-avoid-ai-writing) · [49 humanize-chinese](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/49-voidborne-d-humanize-chinese) |

### 🎼 元编排：⭐ [69 Paper-WorkFlow](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/69-Paper-WorkFlow) —— 一键串起来

[`Paper-WorkFlow`](skills/69-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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/33-Galaxy-Dawn-claude-scholar) → 抓变量定义 / 数据源候选 / 相关文献
3. ⭐ [00 StatsPAI](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_StatsPAI) → 真跑 `sp.feols(...)` + `sp.honest_did(...)`
4. [41 sewage-econometrics-check](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/41-sticerd-eee-sewage-econometrics-check) → 10 项复现包审计 + 稳健性体检
5. ⭐ [00 StatsPAI](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_StatsPAI) + [07 AI-Research-SKILLs](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/07-Orchestra-Research-AI-Research-SKILLs) → 出 Table 1–5 + 期刊级图
6. [38 academic-proofreader](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/38-peternka-academic-proofreader) → 通读 + §comment 标"审稿人会挑刺的位置"
7. [56 econ-writing-skill](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/56-hanlulong-econ-writing-skill) 起草初稿 + ⭐ [48 de-AIGC-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/48-de-AIGC-skills) 🇨🇳🇬🇧 + [45 deslop](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/45-stephenturner-skill-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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.1-Full-empirical-analysis-skill_Python) 📘 | 显式 Python 栈（pandas / statsmodels / linearmodels / pyfixest） |
| [00.2 Full Empirical · Stata](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.2-Full-empirical-analysis-skill_Stata) 📊 | 显式 Stata 栈（reghdfe / ivreg2 / csdid / sdid / rdrobust） |
| [00.3 Full Empirical · R](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.3-Full-empirical-analysis-skill_R) 📗 | 显式 R 栈（tidyverse / fixest / did / HonestDiD）+ Quarto 渲染 |
| [48 de-AIGC-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/48-de-AIGC-skills) 🇨🇳🇬🇧 | 中英双语学术降 AIGC + 去水印层（Turnitin AI / GPTZero / 知网 / 万方 · 隐藏字符 / C2PA / docx 元数据） |
| [50 AER-skills](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/50-brycewang-aer-skills) 📕 | Top-5 经济学投稿套件：识别 → 稳健性 → R&R |
| [69 Paper-WorkFlow](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/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 ↗](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#你究竟得到什么精确数字)）。

### 看到这里 —— 完整 77 行合集目录

[↴ 直跳到下方 77 行总表（每个合集带 `#skill-NN` 锚点）](#-77-个核心-skills-合集一览00--73编号连续无空缺)。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](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md) 里的「按用途分组」与「旗舰流水线」两节。

---

## 🧰 77 个核心 Skills 合集一览（`00 → 73`，编号连续无空缺）

> **打开仓库 → 看见整座库。** 全部 **77 个合集 · 1,107 个 skill**，每一个都已 vendor 进本仓库，由 [`catalog/skills.json`](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`](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`](docs/LICENSE_AUDIT.md)，机器可读版本在 [`catalog/provenance.json`](catalog/provenance.json)。

| # | 合集 | 一句话 | 详情 | 来源 |
|--:|:--|:--|:--|:--|
| ⭐ [00](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00-Full-empirical-analysis-skill_StatsPAI) | **StatsPAI** 🔥 | 因果引擎 · Agent-native Python DSL：`sp.causal(...)` 一行跑闭环（DID/RD/IV/SCM/DML，900+ 函数） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-00) | [brycewang-stanford/StatsPAI](https://github.com/brycewang-stanford/StatsPAI) |
| ⭐ [00.1](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.1-Full-empirical-analysis-skill_Python) | **Full Empirical · Python** 📘 | 显式栈：`pandas` · `statsmodels` · `linearmodels` · `pyfixest` | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-00-1) | [⭐ 本仓库](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) |
| ⭐ [00.2](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.2-Full-empirical-analysis-skill_Stata) | **Full Empirical · Stata** 📊 | `reghdfe` · `ivreg2` · `csdid` · `sdid` · `rdrobust` 复现包 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-00-2) | [⭐ 本仓库](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) |
| ⭐ [00.3](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/00.3-Full-empirical-analysis-skill_R) | **Full Empirical · R** 📗 | tidyverse · `fixest` · `did` · `HonestDiD` + Quarto 渲染 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-00-3) | [⭐ 本仓库](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) |
| [01](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/01-lishix520-academic-paper-skills) | academic-paper-skills | 大纲 → 手稿写作 + 7 维审稿人模拟 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-01) | [lishix520/academic-paper-skills](https://github.com/lishix520/academic-paper-skills) |
| [02](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/02-luwill-research-skills) | research-skills | 医学影像综述、提案、论文转幻灯片 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-02) | [luwill/research-skills](https://github.com/luwill/research-skills) |
| [03](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/03-K-Dense-AI-claude-scientific-skills) | scientific-skills | 假设生成 + 28 个科学数据库 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-03) | [K-Dense-AI/claude-scientific-skills](https://github.com/K-Dense-AI/claude-scientific-skills) |
| [04](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/04-K-Dense-AI-claude-scientific-writer) | scientific-writer | 引用管理 + 科学写作 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-04) | [K-Dense-AI/claude-scientific-writer](https://github.com/K-Dense-AI/claude-scientific-writer) |
| [05](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/05-kthorn-research-superpower) | research-superpower | 系统化检索、筛选与引文溯源 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-05) | [kthorn/research-superpower](https://github.com/kthorn/research-superpower) |
| [06](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/06-fuhaoda-stats-paper-writing) | stats-paper-writing | 端到端 LaTeX 统计论文写作 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-06) | [fuhaoda/stats-paper-writing-agent-skills](https://github.com/fuhaoda/stats-paper-writing-agent-skills) |
| [07](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/07-Orchestra-Research-AI-Research-SKILLs) | AI-Research-SKILLs | 发表级 ML 图表、LaTeX、引文核验 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-07) | [Orchestra-Research/AI-Research-SKILLs](https://github.com/Orchestra-Research/AI-Research-SKILLs) |
| [08](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/08-ndpvt-web-latex-document-skill) | latex-document-skill | 创建 / 编译任意 LaTeX 文档为 PDF | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-08) | [ndpvt-web/latex-document-skill](https://github.com/ndpvt-web/latex-document-skill) |
| [09](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/09-meleantonio-awesome-econ-ai-stuff) | awesome-econ-ai | Python 面板数据分析（`linearmodels`） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-09) | [meleantonio/awesome-econ-ai-stuff](https://github.com/meleantonio/awesome-econ-ai-stuff) |
| [10](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/10-Jill0099-causal-inference-mixtape) | causal-inference-mixtape | DID / IV / RDD / SCM 模板（Cunningham） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-10) | [Jill0099/causal-inference-mixtape](https://github.com/Jill0099/causal-inference-mixtape) |
| [11](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/11-James-Traina-compound-science) | compound-science | 面向定量社会科学的贝叶斯估计 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-11) | [James-Traina/compound-science](https://github.com/James-Traina/compound-science) |
| [12](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/12-pedrohcgs-claude-code-my-workflow) | claude-code-my-workflow | 提交 → PR → 合并的研究工作流（Emory） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-12) | [pedrohcgs/claude-code-my-workflow](https://github.com/pedrohcgs/claude-code-my-workflow) |
| [13](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/13-scunning1975-MixtapeTools) | MixtapeTools | Cunningham 的因果推断工具集与讲义 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-13) | [scunning1975/MixtapeTools](https://github.com/scunning1975/MixtapeTools) |
| [14](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/14-luischanci-claude-code-research-starter) | research-starter | R 中的 IV / DiD / RDD，含完整诊断 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-14) | [luischanci/claude-code-research-starter](https://github.com/luischanci/claude-code-research-starter) |
| [15](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/15-Felpix-Studios-social-science-research) | social-science-research | R 或 Python 端到端数据分析 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-15) | [Felpix-Studios/social-science-research](https://github.com/Felpix-Studios/social-science-research) |
| [16](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/16-hsantanna88-clo-author) | clo-author | 多代理数据分析（R / Stata / Python） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-16) | [hsantanna88/clo-author](https://github.com/hsantanna88/clo-author) |
| [17](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/17-DAAF-Contribution-Community-daaf) | DAAF | 安全意识代理框架（32 条 deny rule） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-17) | [DAAF-Contribution-Community/daaf](https://github.com/DAAF-Contribution-Community/daaf) |
| [18](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/18-jusi-aalto-stata-accounting-research) | stata-accounting | 来自 126 篇 *JAR* 论文的实测 Stata 范式 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-18) | [jusi-aalto/stata-accounting-research](https://github.com/jusi-aalto/stata-accounting-research) |
| [19](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/19-CuellarC05-vera-economic-intelligence) | vera-economic-intelligence | 经济情报 / 政策研究情报工作流 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-19) | [CuellarC05/vera-economic-intelligence](https://github.com/CuellarC05/vera-economic-intelligence) |
| [20](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/20-wenddymacro-python-econ-skill) | python-econ-skill | DSGE / HANK 与定量经济计算 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-20) | [wenddymacro/python-econ-skill](https://github.com/wenddymacro/python-econ-skill) |
| [21](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/21-claesbackman-AI-research-feedback) | AI-research-feedback | 用 AI 同行评审生成结构化反馈 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-21) | [claesbackman/AI-research-feedback](https://github.com/claesbackman/AI-research-feedback) |
| [22](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/22-christopherkenny-skills) | christopherkenny-skills | 面向 Quarto（`.qmd`）的 APSA 风格检查器 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-22) | [christopherkenny/skills](https://github.com/christopherkenny/skills) |
| [23](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/23-Learning-Bayesian-Statistics-baygent-skills) | baygent | 带护栏的 PyMC / Arviz 贝叶斯工作流 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-23) | [Learning-Bayesian-Statistics/baygent-skills](https://github.com/Learning-Bayesian-Statistics/baygent-skills) |
| [24](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/24-Imbad0202-academic-research-skills) | academic-research-skills | 5 审稿人多视角论文评审 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-24) | [Imbad0202/academic-research-skills](https://github.com/Imbad0202/academic-research-skills) |
| [25](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/25-HosungYou-Diverga) | Diverga | 研究问题精炼器（抗模式坍缩） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-25) | [HosungYou/Diverga](https://github.com/HosungYou/Diverga) |
| [26](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/26-Data-Wise-scholar) | scholar | 统计算法设计与文档 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-26) | [Data-Wise/claude-plugins](https://github.com/Data-Wise/claude-plugins) |
| [27](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/27-dariia-m-my_claude_skills) | my_claude_skills | 经济学摘要写作指南 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-27) | [dariia-m/my_claude_skills](https://github.com/dariia-m/my_claude_skills) |
| [28](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/28-maxwell2732-paper-replicate-agent-demo) | paper-replicate-agent | 论文复现代理演示 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-28) | [maxwell2732/paper-replicate-agent-demo](https://github.com/maxwell2732/paper-replicate-agent-demo) |
| [29](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/29-quarcs-lab-project20XXy) | project20XXy | 可复现手稿 + notebook 项目 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-29) | [quarcs-lab/project20XXy](https://github.com/quarcs-lab/project20XXy) |
| [30](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/30-zirui-song-claude-skills) | zirui-song-claude-skills | Zirui Song 的研究辅助 Claude 技能集 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-30) | [zirui-song/claude-skills](https://github.com/zirui-song/claude-skills) |
| [31](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/31-thalysandratos-claude-code-skills) | claude-code-skills | Python 面板数据分析 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-31) | [thalysandratos/claude-code-skills](https://github.com/thalysandratos/claude-code-skills) |
| [32](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/32-dylantmoore-stata-skill) | stata-skill | 高性能 Stata C/C++ 插件 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-32) | [dylantmoore/stata-skill](https://github.com/dylantmoore/stata-skill) |
| [33](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/33-Galaxy-Dawn-claude-scholar) | claude-scholar | 研究全生命周期：选题 → 综述 → 实验 → 审稿回复 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-33) | [Galaxy-Dawn/claude-scholar](https://github.com/Galaxy-Dawn/claude-scholar) |
| [34](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/34-andrehuang-research-companion) | research-companion | 头脑风暴、评估并决策研究方向 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-34) | [andrehuang/research-companion](https://github.com/andrehuang/research-companion) |
| [35](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/35-bahayonghang-academic-writing-skills) | academic-writing-skills | 面向投稿场所的工业 AI 文献研究 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-35) | [bahayonghang/academic-writing-skills](https://github.com/bahayonghang/academic-writing-skills) |
| [36](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/36-taoyunudt-literature-review-skill) | literature-review-skill | 完整文献综述工作流（中文） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-36) | [taoyunudt/literature-review-skill](https://github.com/taoyunudt/literature-review-skill) |
| [37](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/37-IlanStrauss-ai-skills) | IlanStrauss-ai-skills | Ilan Strauss 经济学研究 AI 工作流 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-37) | [IlanStrauss/ai-skills](https://github.com/IlanStrauss/ai-skills) |
| [38](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/38-peternka-academic-proofreader) | academic-proofreader | 学术校对 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-38) | [peternka/academic_proofreader](https://github.com/peternka/academic_proofreader) |
| [39](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/39-vincentarelbundock-marginaleffects) | marginaleffects | 预测、斜率与比较（R / Python） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-39) | [vincentarelbundock/marginaleffects](https://github.com/vincentarelbundock/marginaleffects) |
| [40](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/40-py-econometrics-pyfixest) | pyfixest | Python 中的快速固定效应估计 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-40) | [py-econometrics/pyfixest](https://github.com/py-econometrics/pyfixest) |
| [41](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/41-sticerd-eee-sewage-econometrics-check) | sewage-econometrics-check | 10 项复现包审计 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-41) | [sticerd-eee/sewage](https://github.com/sticerd-eee/sewage) |
| [42](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/42-wanshuiyin-ARIS) | ARIS | 自主「research-in-sleep」代理，端到端 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-42) | [wanshuiyin/Auto-claude-code-research-in-sleep](https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep) |
| [43](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/43-wentorai-research-plugins) | research-plugins | 478 个研究插件：数据可视化、领域、基础设施 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-43) | [wentorai/research-plugins](https://github.com/wentorai/research-plugins) |
| [44](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/44-matsuikentaro1-humanizer_academic) | humanizer_academic | 为医学/学术手稿去 AI 味（34 类模式） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-44) | [matsuikentaro1/humanizer_academic](https://github.com/matsuikentaro1/humanizer_academic) |
| [45](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/45-stephenturner-skill-deslop) | deslop | 去除 AI 写作痕迹（5 维评分） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-45) | [stephenturner/skill-deslop](https://github.com/stephenturner/skill-deslop) |
| [46](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/46-hardikpandya-stop-slop) | stop-slop | 三层 AI 痕迹检测与改写 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-46) | [hardikpandya/stop-slop](https://github.com/hardikpandya/stop-slop) |
| [47](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/47-conorbronsdon-avoid-ai-writing) | avoid-ai-writing | 审计 → 改写 → 二次审计 AI 味（留痕） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-47) | [conorbronsdon/avoid-ai-writing](https://github.com/conorbronsdon/avoid-ai-writing) |
| ⭐ [48](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/48-de-AIGC-skills) | **de-AIGC-skills** 🇨🇳🇬🇧 | 中英双语学术降 AIGC + 去水印层（Turnitin AI / GPTZero / 知网 / 万方 · 隐藏字符 / C2PA / docx 元数据） | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-48) | [⭐ 本仓库](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills) |
| [49](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/49-voidborne-d-humanize-chinese) | humanize-chinese | 检测并人性化 AI 生成的中文文本 | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-49) | [swaylq/humanize-chinese](https://github.com/swaylq/humanize-chinese) |
| ⭐ [50](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/50-brycewang-aer-skills) | **AER-skills** 📕 | Top-5 经济学投稿套件：识别 → 稳健性 → R&R | [→](https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/docs/CONTENT_ZH.md#skill-50) | [brycewang-stanford/AER-skills](https://github.com/brycewang-stanford/AER-skills) |

> The README is long; the above is its first 40000 characters. Full content: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills