Agentic AI coding tools with autonomous capabilities beyond conversational content generation increasingly automate repetitive and time-consuming software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. In this paper, 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 and, in an empirical study of 2,926 GitHub repositories, examine whether and how they are adopted. We then explore Context Files, Skills, and Subagents, that is, three mechanisms available across tools, in more detail. Our findings reveal three trends. 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 longitudinal and experimental research on how configuration strategies evolve and affect agent performance as agentic AI coding tools mature.

翻译：具备超越对话内容生成自主能力的智能AI编程工具正日益自动化重复且耗时的软件开发任务。开发者可通过版本化仓库级构件（如Markdown和JSON文件）配置这些工具。本文系统分析了Claude Code、GitHub Copilot、Cursor、Gemini和Codex等智能AI编程工具的配置机制，识别出八种配置方式，并通过对2,926个GitHub仓库的实证研究，检验了这些方式的采用情况与具体实践。随后，我们深入探讨了跨工具通用的三种机制：上下文文件、技能和子代理。研究发现呈现三大趋势：首先，上下文文件在配置生态中占据主导地位，常作为仓库中的唯一配置机制，其中AGENTS$.$md正成为跨工具的互操作性标准；其次，技能与子代理等高级机制仅被浅层采用——多数仓库仅定义一至两个构件，且技能主要依赖静态指令而非可执行工作流；最后，不同工具正形成差异化的配置文化，其中Claude Code用户采用的机制范围最为广泛。这些发现为纵向研究和实验研究建立了实证基线，有助于考察配置策略如何随智能AI编程工具的发展而演变，并如何影响智能体性能。