Agentic code assistants are a new generation of AI systems capable of performing end-to-end software engineering tasks. While these systems promise unprecedented productivity gains, their behavior and effectiveness depend heavily on configuration files that define architectural constraints, coding practices, and tool usage policies. However, little is known about the structure and content of these configuration artifacts. This paper presents an empirical study of the configuration ecosystem of Claude Code, one of the most widely used agentic coding systems. We collected and analyzed 328 configuration files from public Claude Code projects to identify (i) the software engineering concerns and practices they specify and (ii) how these concerns co-occur within individual files. The results highlight the importance of defining a wide range of concerns and practices in agent configuration files, with particular emphasis on specifying the architecture the agent should follow.

翻译：智能代码助手是新一代能够执行端到端软件工程任务的人工智能系统。尽管这些系统有望带来前所未有的生产力提升，其行为与有效性高度依赖于定义架构约束、编码实践及工具使用策略的配置文件。然而，这些配置制品在结构与内容方面的知识仍相当匮乏。本文对Claude Code（最广泛使用的智能编码系统之一）的配置生态系统进行了实证研究。我们收集并分析了来自公开Claude Code项目的328份配置文件，旨在识别：(i) 它们所指定的软件工程关注点与实践；(ii) 这些关注点如何在单个文件中共存。研究结果凸显了在智能体配置文件中定义广泛关注点与实践的重要性，尤其需要明确指定智能体应遵循的架构规范。