Configuring Agentic AI Coding Tools: An Exploratory Study (Supplementary Material)

Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning a spectrum from static context to executable and external integrations, and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in a repository, with AGENTS.md emerging as an interoperable standard across tools. Second, advanced mechanisms such as Skills and Subagents are only shallowly adopted. Most repositories define only one or two artifacts, and Skills predominantly rely on static instructions rather than executable workflows. Third, distinct configuration cultures are forming around different tools, with Claude Code users employing the broadest range of mechanisms. These findings establish an empirical baseline for understanding how developers configure agentic tools, suggest that AGENTS.md serves as a natural starting point, and motivate longitudinal and experimental research on how configuration strategies evolve and affect agent performance.

Claude Code (Opus 4.6, high reasoning effort) was used to write and revise code and documentation for the pipeline that produced this dataset. The research design and methodology are the work of the human authors.