GitSkills: A Dataset of Agent Skills on GitHub

2026-08-11

AI Summary

This study addresses the lack of systematic empirical research on the authoring, reuse, and maintenance of agent skills for large language models in GitHub repositories. To bridge this gap, we introduce GitSkills, a novel dataset comprising 3,797,117 SKILL.md files collected from 282,200 public repositories. After deduplication via content hashing, the dataset contains 1,877,981 unique skills, each preserving its full textual content, YAML front matter, directory structure, and associated repository metadata. GitSkills is the first large-scale, systematically curated collection of agent skills written in natural language, released in SQLite format to support reproducible research. It establishes a foundational resource for empirical studies at the intersection of software engineering and AI agents, enabling multidimensional analyses of skill adoption, reuse patterns, evolution, provenance, and security.

Abstract

An agent skill is a folder containing a SKILL.md file with instructions for a language-model agent, optionally accompanied by scripts and reference files. The agent loads the skill when it judges that a task matches the skill description. Anthropic introduced the format in October 2025 as an open specification. Nine months later, we find that skill files in the millions sit in public GitHub repositories. Skills are unlike the artifacts the SE research community usually mines: they are written mainly in natural language, a model selects them probabilistically at run time, and no compiler or type checker verifies the selection. They also have no central registry or package manager, so they spread by copying folders between repositories. How developers write, reuse, and maintain skills is therefore an empirical question, and no existing dataset records this population. We present GitSkills, a dataset of 3,797,117 SKILL.md files collected from 282,200 public repositories in July 2026. The dataset retains every file occurrence with its repository, path, and content hash. It groups identical files into 1,877,981 distinct contents and enriches one representative per group with the full text, parsed front matter, folder contents, repository metadata, and, for a subset, the commit history of the file. A single self- contained SQLite file supports research on the adoption, reuse, structure, authorship, maintenance, and security of agent skills.

Problem

• agent skills
• GitHub
• dataset
• natural language
• software reuse

Innovation

• agent skills
• GitSkills dataset
• large language model agents
• empirical software engineering
• natural language artifacts