mlsanityData to 14 Oct 2026

GitSkills: A Dataset of Agent Skills on GitHub

arXiv 2608.10906 ↗First posted 11 Aug 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Agent skill learning and evolution

Abstract (arXiv)

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, public GitHub repositories hold millions of skill files. Skills are unlike the artifacts that software engineering researchers usually mine: they are written mainly in natural language, a model selects them probabilistically at run time, and no compiler or type checker verifies the selection. Skills also have no central registry or package manager; developers reuse them 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. We group identical files into 1,877,981 distinct contents and enrich 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use. Builds on 0 of its 5 references that are in the map. First posted on arXiv 11 Aug 2026.

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  1. 2026Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality
  2. 2026SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents
  3. 2026From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintained
  4. 2026From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skills
  5. 2026AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent
  6. 2026Skilldex: A Package Manager and Registry for Agent Skill Packages with Hierarchical Scope-Based Distribution
  7. 2026Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains
  8. 2026SkillScriptBench: Benchmarking Self-Evolution of Executable Agent Skill Packages Beyond Markdown