Auto-Empirical Research Skills plugin
Auto-Empirical Research Skills (AERS)
A plugin marketplace for empirical social science research: causal inference, econometrics and manuscript prep for top journals
- 5,007 ★ on the repository
- Last commit
Extended access
Why this level
- Runs statistical code in Python, Stata and R and reads project data
- Part of the catalog is attached as third-party submodules with their own licenses
Install
- Works in
Manual install
claude plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills
claude plugin install aer-skills@auto-empirical-research-skills
claude plugin install empirical-analysis-python@auto-empirical-research-skills
claude plugin install empirical-analysis-stata@auto-empirical-research-skills
claude plugin install empirical-analysis-r@auto-empirical-research-skillsInstall only the plugins you need from the list. Requires Claude Code 2.1 or newer.
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- Runs statistical code in Python, Stata and R and reads project data
- Part of the catalog is attached as third-party submodules with their own licenses
This is third-party code. Review the repository files before installing.
What it does
The project installs into Claude Code and Codex as a plugin marketplace for empirical research. The authors' own core plugins are aer-skills, a nine-skill stack for preparing an economics manuscript for top journals, from topic selection and identification strategy to robustness, tables and revision letters; plus three empirical-analysis pipelines in Python, Stata and R with steps for data cleaning, variable construction, model estimation, robustness and table and figure output. Identification methods cover difference-in-differences, instrumental variables, regression discontinuity, synthetic control and double machine learning. Around these first-party pipelines sits a curated catalog of third-party skills you can attach by copying into the skills directory or via --plugin-dir. The project ships benchmarks and eval scenarios so the agent's work can be re-checked.
Who it is for. For economists and social science researchers who run empirical work and prepare papers for publication.
Good fit when
- You need an empirical analysis pipeline in Python, Stata or R with robustness checks
- You need causal estimation: difference-in-differences, instruments, regression discontinuity, synthetic control
- You need to bring a manuscript to a top economics journal format
Not a fit when
- The task is not about empirical or statistical data analysis
- You need a single small skill without installing a large marketplace
Example request
Run a Callaway and Sant'Anna event study on this panel data and build the paper tablesLimitations
The analysis pipelines require installed environments: Python with econometrics packages, Stata or R with Quarto. The project targets economics and social science first, so methods need adaptation for other fields. Part of the catalog is third-party skills attached as submodules, with their own quality and licenses. Marketplace plugin installation needs a recent Claude Code version. The agent helps with method and formatting, but responsibility for the correctness of conclusions and data rests with the researcher. The third-party catalog also contains sets that lower the detectability of AI text for checking systems and strip watermarks and document metadata. The econometric pipelines do not need them and we do not recommend them: presenting AI-written text as handwritten breaks the rules of many journals and universities. If you use AI to prepare a paper, disclose it under the rules of the publisher.
How to disable. Remove the installed plugins via /plugin and drop the auto-empirical-research-skills marketplace. Delete any manually copied folders from ~/.claude/skills/ or the project's .claude/skills/.
FAQ
Is this just a list of other people's skills?
No. The project has its own installable plugins: aer-skills and three analysis pipelines in Python, Stata and R. A catalog of third-party skills sits around them.
Do I need Stata?
Only for the Stata pipeline. There are separate Python and R with Quarto pipelines; pick the one for your stack.
Discussion
No questions or reviews yet.
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Comparison with similar tools
Cards that share the main search query, and how they differ.
| Tool | Official | Authentication | Repository stars | Main difference |
|---|---|---|---|---|
| Auto-Empirical Research Skills (this card) | no | 5,007 | ||
| StatsPAI: causal inference and econometrics for agents | no | not required | 374 | the MCP server and skill of the StatsPAI library |
| mathmodel-skill | no | 298 | a skill that walks a student team through a math modeling contest in | |
| AI Research Skills | no | 651 | a marketplace of five Claude Code plugins for researchers |
README in short
The README presents this Stanford REAP and CoPaper.AI project as an end-to-end empirical research process: from topic framing and literature review to data collection, identification strategy, estimation, robustness, table and figure output, writing and reviewer responses. Documentation is multilingual, Chinese by default with an English version. Installation goes through the Claude Code plugin marketplace, and individual skills can be copied into the skills directory or attached via --plugin-dir. The project keeps benchmarks and eval scenarios so results can be re-checked with the same scorer. First-party plugins and parts of the catalog are under MIT and CC-BY-SA.