Summary

This repository curates over 23,000 AI agent "Skills" for empirical research across eight social science disciplines, aiming to streamline the entire research workflow from topic selection to journal submission. It provides a structured collection of tools and methodologies, enabling AI agents to execute complex research tasks reproducibly and efficiently, with a notable focus on accelerating paper generation and enhancing research reproducibility.

How It Works

The core concept is "Skills," which encode the methodological expertise of senior researchers into structured workflows for AI agents. Instead of step-by-step prompting, agents can execute a complete analysis (e.g., a full Difference-in-Differences analysis) by leveraging these pre-defined skills. The collection organizes these skills by research stages, facilitating their integration into AI-driven research platforms like CoPaper.AI, which promises to generate reproducible empirical papers in approximately 20 minutes.

Quick Start & Requirements

This repository serves as a curated index of AI agent "Skills" rather than a single installable package. For immediate use, CoPaper.AI offers a platform for generating empirical papers in approximately 20 minutes. The StatsPAI Python package, an agent-native econometrics library, can be installed via pip (pip install StatsPAI) and is MIT licensed. Specific skill integrations may require compatible AI agent frameworks or environments. Links: CoPaper.AI, StatsPAI GitHub.

Highlighted Details

• Comprehensive collection of 23,000+ AI agent skills sourced from 119 GitHub repositories.
• Covers 8 social science disciplines, organized by research workflow stages (topic selection to submission).
• Features StatsPAI, an MIT-licensed, JOSS-published Python package for causal inference and econometrics with over 390 functions.
• Includes specialized skills like chinese-de-aigc for reducing AI detection in Chinese academic papers and others for de-AIGC detection in English.
• Emphasis on reproducible research and accelerating paper generation, with claims of enabling mainstream journal-level paper production in 20 minutes.

Maintenance & Community

The repository is maintained by the CoPaper.AI team from Stanford REAP. Community contributions are welcomed, particularly for social science, causal inference, business, and Chinese-language skills.

Licensing & Compatibility

The StatsPAI package is released under the MIT license, permitting commercial use and integration into closed-source projects. The licensing for the broader collection of individual skills is not uniformly specified within this repository and may vary across the original source repositories. Users should verify the license of each skill they intend to use.

Limitations & Caveats

The collection is a curated list of external resources, implying that the functionality and maintenance of individual skills depend on their original repositories. Specific requirements (e.g., AI frameworks, Python versions) may vary. The effectiveness of de-AIGC skills is subject to evolving detection algorithms and may require adaptation.