brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research

• highreadme#1

  Reposition README H1 and opening paragraph to clarify it's a curated collection/list, not a deployable toolkit.

  原因：

  当前

  # Auto-Empirical Research Skills (AERS)
  
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  **🌐 Language: English | [简体中文](README-zh-CN.md) | [繁體中文](README-zh-TW.md) | [日本語](README-ja.md) | [한국어](README-ko.md)**
  
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          <a href="https://copaper.ai"></a>
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    <strong>Stanford REAP × CoPaper.AI</strong> · An academic–industrial AI toolkit for empirical research<br/>
    <sub>Built by Stanford's empirical-methodology team — the full pipeline from data cleaning to top-journal submission</sub>
  
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  复制粘贴的修复

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  # Awesome Agent Skills for Empirical Research (AERS) - A Curated Collection
  
  <div align="center">
  
  **🌐 Language: English | [简体中文](README-zh-CN.md) | [繁體中文](README-zh-TW.md) | [日本語](README-ja.md) | [한국어](README-ko.md)**
  
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  This repository is a **curated collection of 23,000+ agent skills** for empirical research across social science disciplines, maintained by Stanford REAP × CoPaper.AI. It is an academic–industrial resource providing a comprehensive skills distribution, **not a deployable AI toolkit or library**.

  待办完成忽略
• mediumlicense#2

  Add a clear statement about the repository's license(s) to the README.

  原因：

  复制粘贴的修复

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  ## License
  
  This repository is licensed under [Specify License Name(s) and terms, e.g., a custom license, or a combination of licenses]. Please refer to the `LICENSE` file for full details.

  待办完成忽略
• lowreadme#3

  Clarify the nature of the 'skills' in the collection (e.g., links, descriptions, not deployable code).

  原因：

  当前

  The empirical-research specialist's agent-skills distribution. Not a marketing list — **1,080 skills vendored and cataloged** in this repo, wrapped in a **numeric benchmark, an eval harness, a security audit, and CI**, plus a curated map of **23,000+ skills across 119 repositories** in the wider ecosystem.

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  This repository is an empirical-research specialist's agent-skills distribution. It is **a curated catalog of 1,080 detailed skill descriptions and external references**, not a deployable code library. The catalog includes a numeric benchmark, an eval harness, a security audit, and CI for quality assurance of the *curation process*, alongside a curated map of 23,000+ skills across 119 repositories in the wider ecosystem.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find AI agent skills for empirical research in social science fields?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers
  2. Hugging Face Datasets
  3. spaCy
  4. NLTK
  5. scikit-learn
  6. NetLogo
  7. OpenAI API
  8. quanteda
  9. tm
  10. tidytext

  AI 推荐了 10 个替代方案，却始终没点名 brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Looking for a comprehensive library of agent skills to enhance reproducible academic research.

  你：未被推荐
  AI 推荐顺序：

  1. LangChain
  2. LlamaIndex
  3. Haystack
  4. AutoGPT
  5. CrewAI
  6. AutoGen

  AI 推荐了 6 个替代方案，却始终没点名 brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research?

  pass
  ⚠AI 未点名 brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research solve, and who is the primary audience?

  pass
  ⚠AI 未点名 brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？