A Dataset of Agentic AI Coding Tool Configurations

TL;DR

This paper presents a large, curated dataset of agentic AI coding tool configurations from open-source repositories, enabling research on AI tool usage, context engineering, and human-AI collaboration.

Contribution

It introduces a comprehensive dataset of 15,591 configuration artifacts and 148,519 AI-co-authored commits across multiple AI coding tools, filling a gap in available resources.

Findings

Dataset covers 4,738 repositories across five AI tools.

Includes full content of 18,167 configuration files.

Supports research on AI tool adoption and collaboration patterns.

Abstract

Agentic AI coding tools such as Claude Code and OpenAI Codex execute multi-step coding tasks with limited human oversight. To steer these tools, developers create repository-level configuration artifacts (e.g., Markdown files) for configuration mechanisms such as Context Files, Skills, Rules, and Hooks. There is no curated dataset yet that captures these configurations at scale. This dataset, collected from open-source GitHub repositories, fills that gap. We selected 40,585 actively maintained repositories through metadata filtering, classified them using GPT-5.2 to identify 36,710 as belonging to engineered software projects, and systematically detected configuration artifacts in these repositories. The dataset covers 4,738 repositories across five tools (Claude Code, GitHub Copilot, OpenAI Codex, Cursor, Gemini) and eight configuration mechanisms. We collected 15,591 configuration artifacts, the full content of 18,167 configuration files associated with these configuration artifacts, and 148,519 AI-co-authored commits. The dataset and the construction pipeline are publicly available on Zenodo under CC BY 4.0. An interactive website allows researchers to browse and explore the data. This data supports research on context engineering, AI tool adoption patterns, and human-AI collaboration.