REPOGEO REPORT · LITE

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

Default branch main · commit 7f52b28b · scanned 6/18/2026, 1:22:12 PM

GitHub: 1,929 stars · 278 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

27 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

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

Why:

CURRENT

# Auto-Empirical Research Skills (AERS)

<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)**

<br/>

  

  <br/>

  <table>
    <tr>
      <td align="center">
        <a href="https://copaper.ai"></a>
      </td>
      <td width="60"></td>
      <td align="center">
        
      </td>
    </tr>
  </table>

  <br/>

  <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>

  <br/>
</div>

COPY-PASTE FIX

# 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)**

<br/>

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**.

mediumlicense#2

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

Why:

COPY-PASTE FIX

## 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.

lowreadme#3

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

Why:

CURRENT

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.

COPY-PASTE FIX

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.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
Hugging Face Datasets · recommended 1×
spaCy · recommended 1×
NLTK · recommended 1×
scikit-learn · recommended 1×

CATEGORY QUERY
Where can I find AI agent skills for empirical research in social science fields?
you: not recommended
AI recommended (in order):
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 recommended 10 alternatives but never named brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a comprehensive library of agent skills to enhance reproducible academic research.
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. AutoGPT
5. CrewAI
6. AutoGen
AI recommended 6 alternatives but never named brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research?
pass
AI did not name brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research in production, what risks or prerequisites should they evaluate first?
pass
AI named brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
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 did not name brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite