Problem

Agentic code assistants represent a significant evolution in AI systems for software engineering, moving beyond simple code completion to end-to-end task execution. Despite their growing popularity, there is limited understanding of how developers configure these agents, specifically regarding the structure and content of their configuration files. These files are crucial for defining architectural constraints, coding practices, and tool usage, directly impacting the agent's behavior and effectiveness. This study aims to fill this gap by investigating the configuration ecosystem of Claude Code.

Method

The study involved an empirical analysis of 328 configuration files (Claude.md) collected from public, popular, and active Claude Code projects on GitHub between August 28-30, 2025. Repositories with fewer than 100 stars, non-English content, or those not representing real-world applications were filtered out. The content of these Markdown files was then parsed, focusing on level-2 (and some level-1) section titles, which were manually grouped into semantically related software engineering concerns and practices. Finally, the FP-Max algorithm, with a minimum support of 0.15, was used to identify frequently co-occurring concerns and practices, revealing common configuration patterns.

Results

The analysis revealed that configuration files primarily define software architecture (72.6% of files), followed by general development guidelines (44.8%), project overview (39%), testing guidelines (35.4%), and commands (33.2%). Other recurring themes included dependencies, general project guidelines, integration, and configuration. While code examples were notably present in Development Guidelines sections (17.68% of instances in that category), links were most frequent in Architecture sections (1.83% of files), and diagrams were rarely found. The most common configuration pattern observed included rules for Architecture, Dependencies, and Project Overview, appearing in 21.6% of the files, with Architecture being a consistent element across the top-5 patterns.

Implications

This research underscores the critical role of comprehensive configuration files in guiding AI coding agents, particularly in specifying architectural guidelines. By understanding common concerns, practices, and patterns, developers can more effectively configure these agents to align with project requirements and best practices. The findings provide insights for developers using or planning to use agentic systems, helping them to configure their code agents more effectively. Future work includes analyzing discussions and evolution of these configuration files, and developing tools to recommend best practices for writing them.