The `Auto-Empirical-Research-Skills` GitHub repository, developed by brycewang-stanford and maintained by CoPaper.AI from Stanford REAP, provides an extensive collection of over 23,000 AI agent skills. These skills are categorized for empirical research across eight social science disciplines, including economics, political science, sociology, and psychology. The project aims to support the entire research workflow, from topic selection and data analysis to robust testing and journal submission. The repository also highlights its integration with CoPaper.AI, which claims to facilitate the generation of reproducible empirical papers. The project emphasizes a "three-layer credit anchor" approach, combining academic rigor from Stanford REAP, engineering implementation through the AI assistant, and an open-source engine (StatsPAI) for causal inference. The repository has undergone security scans, with all 52 skills and over 2,940 files reported as clean, with no flagged malicious content. Community contributions are welcomed, with a process in place for reviewing and integrating valuable skills.
Auto-Empirical-Research-Skills: A Curated Collection of 23,000+ AI Agent Skills for Social Science Research
The `Auto-Empirical-Research-Skills` GitHub repository offers a curated collection of over 23,000 AI agent skills designed for empirical research across eight social science disciplines. Maintained by CoPaper.AI from Stanford REAP, this resource aims to streamline the research workflow from data analysis to paper submission.
Why it matters
This repository is significant for researchers in social sciences, providing a structured library of AI agent skills that can automate and standardize various stages of empirical research. Its focus on reproducibility and integration with tools like CoPaper.AI could enhance research efficiency and quality.