# Auto Empirical Research Skills

Route empirical-research requests through the Auto-Empirical Research Skills catalog...

- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars)
- Category: Documents
- License: CC-BY-SA-4.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill auto-empirical-research-skills -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill auto-empirical-research-skills -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill auto-empirical-research-skills -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill auto-empirical-research-skills -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills`

## Description

Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows, or an end-to-end run from raw data all the way to a finished Word (.docx) manuscript, without reading the entire repository at once.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Auto-Empirical Research Skills Router

Use this root skill when the full AERS repository has been installed as a single skill folder. Treat it as a router and catalog, not as a request to load every vendored `SKILL.md`.

The catalog holds **1,151 skills across 78 vendored collections**. Never read them all, route to one, then load only that skill's `SKILL.md`.

## Workflow

1. Classify the user's empirical-research task by **stage**, then load the single best-matching skill:
   - Full pipeline or orchestration: start with `skills/69-Paper-WorkFlow/` or the `skills/00*` flagship analysis skills, `skills/00-Full-empirical-analysis-skill_StatsPAI/` (StatsPAI), `skills/00.1-Full-empirical-analysis-skill_Python/` (Python), `skills/00.2-Full-empirical-analysis-skill_Stata/` (Stata), `skills/00.3-Full-empirical-analysis-skill_R/` (R). Note the StatsPAI flagship has no dot in its prefix, so a `skills/00.*` glob misses it.
   - Causal inference and econometrics: pick by method from the table below, or search `catalog/skills.json` / `docs/TAXONOMY.md`.
   - AER or top economics journal work: start with `skills/50-brycewang-aer-skills/`.
   - Replication, citation, or peer review: use `docs/SKILL_CATALOG.md` and `docs/GOLDEN_WORKFLOWS.md` to choose a focused skill.
   - Academic de-AIGC (English or Chinese) or academic rewriting: start with `skills/48-de-AIGC-skills/` or nearby writing skills in the catalog.
2. Read only the selected child skill's `SKILL.md`, then follow its progressive-disclosure instructions for `references/`, `scripts/`, `assets/`, or templates.
3. If no child skill clearly matches, run the ranked catalog search first, it takes English or Chinese task text:

     ```bash
     python3 scripts/find-skill.py "staggered difference-in-differences event study"
     python3 scripts/find-skill.py "降低中文论文 AIGC 率" -k 5
     ```

The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD//SKILL.md