Automates empirical research with 23,000 AI agent skills https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills…

brycewang-stanford/Auto-Empirical-Research-Skills

Source: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills

Auto-Empirical Research Skills (AERS, 23K+ Skills)

Repository renamed → now “Auto”. This project started life as Awesome Agent Skills for Empirical Research and has been renamed to Auto-Empirical-Research-Skills (AERS). The new name reflects the core idea: not just a collection of skills, but an agent that automatically runs the full empirical-research pipeline end to end — from raw data cleaning → identification & estimation → robustness checks → tables, figures, and a submission-ready draft — with minimal human hand-holding.

GitHub automatically redirects the old URL, but please update your bookmarks and local remote:

git remote set-url origin https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git

🌐 Language / 语言: English | 中文

Stanford REAP × CoPaper.AI · An academic-industrial AI toolkit for empirical research
_{Crafted by Stanford’s empirical methodology team — covering the full pipeline from data cleaning to top-journal submission}

The Definitive Collection of AI Agent Skills for Empirical Research — 119 GitHub Repos / 23,000+ Skills

A curated, opinionated list of 119 GitHub repositories and 23,000+ AI Agent Skills for empirical research in economics, political science, sociology, psychology, public health, education, management, finance, and public policy — organized by research workflow, from topic selection to journal submission.

In 2026, the way we do empirical research is being redefined.

CoPaper.AI — an empirical research AI assistant incubated by researchers at Stanford REAP / SCCEI (Stanford Center on China’s Economy and Institutions) — can complete a publication-quality empirical paper in 20 minutes: from data import, descriptive statistics, causal inference models, and robustness checks to formatted result tables, all in one go. The secret isn’t a more powerful model — it’s Skills: encoding senior researchers’ methodological expertise into structured workflows, so the AI knows “what a complete DID analysis should include” instead of waiting for you to remind it step by step.

This repository is the Agent Skills landscape we compiled while building CoPaper.AI. We organized hundreds of Skills repos and tens of thousands of Skills scattered across GitHub, communities, and academia by research workflow stages, so you can pick what you need.

🎓 Three Layers of Trust · Why It’s Us Building This

🔒 Use with confidence: every one of the 52 Skills / 2,940+ files in this repo passed our systematic security audit — 52/52 CLEAN, zero FLAGGED, zero exfiltration, zero reverse shells, zero prompt injection.

💡 Want it out of the box? Skip the Skills assembly — try → copaper.ai and let the Stanford methodology team run the empirical pipeline end-to-end for you.

Start Here

• Search the local index: docs/search.html
• Browse the generated local catalog: docs/SKILL_CATALOG.md
• Copy a ready-to-run empirical workflow: docs/GOLDEN_WORKFLOWS.md
• See flagship demos: docs/demos/
• Run flagship regression prompts: docs/EVALS.md
• Install or copy skills into an agent runtime: docs/INSTALL.md
• Use the machine-readable index: catalog/skills.json
• Coordinate parallel agent work: docs/AGENT_COORDINATION.md
• Check provenance and license risk: docs/LICENSE_AUDIT.md
• Check contribution and validation rules: docs/QUALITY_GATE.md · docs/SKILL_SUBMISSION_GUIDE.md
• See the repo audit and improvement plan: docs/REPO_AUDIT_2026-05-31.md · docs/ROADMAP.md
• Rebuild and validate locally:

make catalog
make validate

Pick a Workflow in 30 Seconds

🆕 Changelog

Table of Contents

• Start Here
• 🆕 Changelog
• What Can This List Do for You?
• Quick Lookup by Research Stage
• Skills by Category
  • 01 - Topic Selection & Research Design
  • 02 - Literature Search & Review
  • 03 - Paper Reading & Analysis
  • 04 - Data Collection & Cleaning
  • 05 - Statistical Analysis & Causal Inference
  • 06 - Paper Writing
  • 07 - Paper Revision & Polishing
  • 08 - Citation Management & Typesetting
  • 09 - Replication & Reproducible Research
  • 10 - Peer Review Response & Defense
• Comprehensive Skill Suites
  • 🚨 Anti-AIGC Detection & De-AI Academic Writing (Highlighted)
• Multi-Agent Collaboration Systems
• Skill Aggregation Platforms & Discovery Tools
• Learning Resources
• 🛡️ Security Scan
• Contributing

What Can This List Do for You?

If you do empirical research, you’ve probably experienced these scenarios:

• You ask AI to run a DID, and it gives you the baseline regression and stops. You say “parallel trends?” — it adds one. “Placebo test?” — another one. “Event study plot?” — yet another. Every time, it’s like squeezing toothpaste.
• You finally finish a draft, but citations are a mess, with a few hallucinated references mixed in.
• You want to replicate an identification strategy from a top journal, but the gap between understanding it and implementing it feels like a mountain.

The problem isn’t that AI can’t do it — it doesn’t know what a complete workflow should include.

A Skill solves this: it’s a methodological playbook for AI. With a Skill, AI knows “running DID means first testing parallel trends, then baseline regression, then 4 robustness checks, then heterogeneity analysis, then mechanism analysis, with specific output formats at each step.” You just say “run a DID analysis” and it follows the complete workflow.

This list helps you find the best Skills for every stage of the empirical research workflow.

Quick Lookup by Research Stage

Not sure which Skill to use? Start from your current research stage:

Topic Ideation → Lit Search → Deep Reading → Research Design → Data Collection
      │              │             │              │                │
      ▼              ▼             ▼              ▼                ▼
     01             02            03             01               04

Data Cleaning → Statistical Analysis → First Draft → Revision → Typesetting
      │              │                    │            │            │
      ▼              ▼                    ▼            ▼            ▼
     04             05                   06           07           08

Replication → Submission → Peer Review Response → Defense
      │           │              │                   │
      ▼           ▼              ▼                   ▼
     09          10             10                  10

One-Stop Solutions

If you don’t want to pick Skills one by one, these solutions cover the full workflow:

Comprehensive Skill Suites

These repositories contain multiple Skills and typically cover several research stages:

Academic Research

Economics / Causal Inference

🚨 Anti-AIGC Detection & De-AI Academic Writing (Highlighted)

This is one of the most critical pain points in academic writing in 2026. Papers failing AIGC detection can be rejected outright, and detectors like Turnitin, GPTZero, and China’s CNKI are getting stricter. The 4 skills below are the most authoritative and complete solutions on GitHub — all MIT open-source, and all locally archived in this repo (skills/44-47).

Recommended combos:

• 🇨🇳 Chinese academic papers (CNKI/Wanfang/VIP) → chinese-de-aigc (original) + revision-guard
• 🇬🇧 English academic papers → humanizer_academic + revision-guard (prevent over-refinement)
• Bilingual papers → chinese-de-aigc + humanizer_academic combined
• Need auditable workflow → avoid-ai-writing (structured reports)
• General writing → stop-slop (5-dim scoring for quantified improvement)

Finance & Investment Research

Education & Public Health

Governance, Compliance & Law

Marketing & Consumer Behavior

Product Management & Organizational Behavior

General Agent Capabilities

Multi-Agent Collaboration Systems

A single Skill solves a point problem; multi-agent systems solve end-to-end workflows. These systems let multiple AI roles divide work, cross-review, and produce output quality far beyond what a single agent can achieve:

Paper Revision & Writing

Why multi-agent beats single agent? When the same agent writes and reviews, it tends to approve its own work. Role separation means the reviewer is independent from the drafter — forming a genuine quality loop. Same logic as academic peer review.

Data Analysis & Research

Academic Data MCP Servers

Skill Aggregation Platforms & Discovery Tools

Don’t know where to find Skills? These platforms are your starting point:

Learning Resources

Official Documentation

• Claude Code Skills Complete Guide — Anthropic’s official 32-page guide
• Agent Skills Standard Specification
• Claude Code Official Docs

Academic Talks & Courses

• AI Agents for Economics Research — Aniket Panjwani, CEPR/VoxDev, 2026.03
• Claude Code & Cowork for Academic Research — A Practical Guide — Practical guide for economists and social scientists, 2026.02
• Building Claude Code Workflow for Economics Scholars — Building Claude Code workflows for economics researchers

Causal Inference Textbooks

• Causal Inference for the Brave and True — Chinese translation, Python code
• Statistical Tools for Causal Inference — Open-source textbook
• Causal Inference and Machine Learning Book

Survey Papers & Awesome Lists

• A Survey of Data Agents — Data agent survey (HKUST)
• From AI for Science to Agentic Science — arXiv:2508.14111
• From Automation to Autonomy — LLM scientific discovery survey (EMNLP 2025)
• Awesome Agents for Science — Papers on LLMs and agents in scientific R&D
• Awesome AI for Science — AI tools, papers, datasets for accelerating scientific discovery
• Awesome AI for Economists — AI tools, libraries, and resources for economics (OpenEcon team)
• Awesome Econ AI Stuff — AI Skills collection for economists, follows SKILL.md standard
• AI for Grant Writing — Curated resources for LLM-assisted grant writing
• Awesome AI Scientist Papers — AI scientist / robot scientist papers
• FreedomIntelligence/OpenClaw-Medical-Skills — 869 medical AI Skills, covering epidemiology, public health, biostatistics

Community & References

• Awesome Claude Skills — Community curated
• Awesome Claude Skills (ComposioHQ) — Curated Claude Skills list
• Awesome Claude Skills (BehiSecc) — Curated Claude Skills list
• Awesome Claude Code — Skills, Hooks, slash commands, agent orchestrators
• Reddit r/ClaudeCode
• Anthropic Claude Code Skills Cookbook — Financial applications Skills tutorial

🛡️ Security Scan

We ran a systematic security audit across all 52 Skills / 2,940+ files in this repository — 52/52 CLEAN, zero FLAGGED. Every “sensitive” hit was verified as legitimate content. No malicious prompts, viruses, trojans, or reverse shells were found. Bottom line: every Skill in this repo is safe to use.

Six-phase defense-in-depth methodology:

1. Automated pattern scan — grep across 13 risk categories (pipe-to-shell, reverse shell, credential exfil, decode-and-run, mining/RAT signatures, prompt injection, etc.)
2. Hook & permission matrix audit — 100% manual review of all 6 hook-bearing Skills and their 40+ hook scripts. Permission allow-lists are restricted to research tooling — no Bash(*) wildcards anywhere.
3. Three parallel agent content audits — independent reviews of SKILL.md prose, agent definitions, and reference documentation for prompt injection, backdoors, hidden Unicode, suspicious package sources.
4. Supplemental integrity checks — hidden characters, encoding anomalies, ultra-long lines, HTML injection, network-related imports, high non-ASCII ratios.

Result distribution: every hit fell into one of three legitimate categories:

• 🛡️ Defensive security rules — deny rules, bash-safety hooks, credential detectors. 17-DAAF is the strongest “security-aware” reference in this batch (14 defensive hooks + 32 deny rules + active credential scanning).
• 📚 Legitimate academic API calls — arXiv / CrossRef / PubMed / Semantic Scholar / FRED / World Bank / OECD / BLS, etc.
• 🔁 Standard Claude Code workflow hooks — project scaffolding, state save/restore, context monitoring, session archive, pre-commit reminders — all local file operations, zero network IO.

Key insight: largest size ≠ highest risk. The Top 5 largest Skills (43-wentorai 478 files / 33-Galaxy-Dawn 327 files / 17-DAAF 319 files / 35-bahayonghang 264 files / 18-jusi-aalto 126 files) all passed full audit, with 17-DAAF actually setting the bar for security-conscious design.

The full report includes Phase 1-6 methodology, a per-Skill audit table for all 52 Skills, and 5 visual infographics: 📋 SECURITY-SCAN-REPORT.md

Contributing

Contributions welcome! Please read CONTRIBUTING.md to learn how to submit new Skill recommendations.

We especially welcome:

• Skills for social science disciplines: economics, political science, sociology, psychology, education, public administration, public health
• New Skill implementations for causal inference methods
• Skills for business disciplines: finance, management, marketing, law & economics
• General academic Skills: literature review, grant writing, research proposals
• MCP servers (academic databases, government data APIs)
• Chinese-friendly Skills
• Multi-agent collaboration system case studies

Star History

If this list helps you, please give it a Star so more researchers can find it.

AI is an amplifier, not a replacement. It handles the heavy lifting; you keep the core judgment.

_{Stanford REAP × CoPaper.AI · An academic-industrial AI toolkit for empirical research}

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20 built-in methodology Skills · 20-minute empirical paper · Powered by StatsPAI (900+ functions, MIT)

Maintained by CoPaper.AI, incubated at Stanford REAP / SCCEI | AI Assistant for Empirical Research