複数のAIコーディングエージェントが混在するリポジトリで、ルールをどう整備するか This article covers strategies for organizing rule files when multiple AI coding agents co…

As development teams adopt more than one AI coding assistant, a practical problem quietly emerges: each tool reads its own configuration or "rules" file, and keeping those files aligned inside a single repository is harder than it looks. This article, published on Zenn, examines strategies for organizing rule files when agents such as Cursor coexist with other assistants in the same codebase, and why consistency matters for teams that want predictable behavior regardless of which tool a developer happens to open.

Rules files are the mechanism by which these agents receive persistent, project-specific instructions. Rather than repeating the same guidance in every prompt, developers encode conventions once: coding style, preferred libraries, directory structure, testing expectations, and constraints such as "do not edit generated files." The agent then loads this context automatically. The difficulty is that the major tools have not converged on a common format or location, so a repository used by several agents can accumulate overlapping, and sometimes contradictory, instruction files.

The fragmentation is concrete. Cursor has moved from a single .cursorrules file to a .cursor/rules directory containing .mdc files, which pair Markdown content with metadata that can scope a rule to specific file globs or mark it to always apply. Claude Code reads a CLAUDE.md file at the repository root, and can also pick up nested versions in subdirectories. GitHub Copilot looks for .github/copilot-instructions.md. Windsurf has used .windsurfrules and a .windsurf/rules directory. Because each of these lives in a different place and follows slightly different conventions, editing one does not update the others, and the instructions can drift apart over time.

The core recommendation in situations like this is to establish a single source of truth rather than maintaining each file by hand. One common pattern is to write the canonical guidance once, in a neutral document, and then propagate it to each tool's expected location. Teams achieve this in a few ways. Some use symbolic links so that multiple filenames point at the same underlying content, which works when the tools accept identical Markdown. Others use a small generation script, run manually or in continuous integration, that reads the master file and writes out each agent-specific variant, applying any format differences such as Cursor's metadata front matter. A lighter approach is to keep the shared rules in one file and have the tool-specific files simply reference or import it where the tooling supports inclusion.

An important piece of adjacent context is the emergence of AGENTS.md as an attempt at a more universal convention. Backed by several projects and tools, including OpenAI's Codex, AGENTS.md aims to be a tool-agnostic place to describe how an agent should operate in a repository. It does not yet replace the proprietary formats, and support varies, so in practice it appears to function as one more file to keep synchronized rather than a complete solution. Still, its existence signals that the ecosystem is aware of the coordination cost and is likely to move toward shared standards over time.

Beyond file mechanics, the article's framing points to a broader discipline that is worth internalizing. Rules management sits alongside prompt engineering and context management as part of how teams get reliable output from AI tools. Overloading an agent with lengthy, redundant instructions can dilute the context window and degrade results, so the goal is not merely to duplicate rules everywhere but to keep them concise, scoped, and current. Treating these files as versioned artifacts, reviewed like any other code and kept in the repository rather than in individual developer settings, helps ensure that new contributors and new tools inherit the same expectations.

For teams standardizing on a multi-agent workflow, the practical takeaway is to decide early where the authoritative rules live, automate their distribution to each tool's required path, and add a check that flags when the generated files fall out of date. That modest investment reduces the risk of one agent following stale conventions while another follows the intended ones, which is exactly the kind of subtle inconsistency that is easy to introduce and difficult to debug later.