A Dataset of Agentic AI Coding Tool Configurations

2026-05-08

AI Summary

This study addresses the scarcity of large-scale, structured configuration data for agentic AI programming tools, which has hindered in-depth exploration of prompt engineering and human-AI collaboration. We present the first systematically constructed and openly released dataset of tool configurations for agentic AI coding, encompassing five major tool categories and eight configuration mechanisms, sourced from 4,738 GitHub repositories. The dataset includes 15,591 configuration entries, 18,167 full configuration files, and 148,519 AI-assisted commit records. Leveraging metadata filtering, GPT-assisted categorization, and configuration extraction techniques, this work delivers a high-quality resource accompanied by an interactive browsing platform to support cutting-edge research in context engineering, AI tool adoption patterns, and human-AI co-development. The data and construction pipeline are publicly available under the CC BY 4.0 license on Zenodo.

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.

Problem

• Agentic AI
• coding tools
• configuration artifacts
• dataset
• human-AI collaboration

Innovation

• Agentic AI
• Configuration Dataset
• Context Engineering
• AI Coding Tools
• Human-AI Collaboration