AIエージェントのルールファイル乱立問題—AGENTS.mdを単一ソースにして全部生成するTo address the fragmentation of rule files across multiple AI agent tools, the…

As AI coding assistants proliferate, developers increasingly face a mundane but real problem: nearly every tool wants its own configuration file to capture project conventions, coding standards, and behavioral guidance. A blog post on Zenn tackles this fragmentation directly, proposing that teams maintain a single AGENTS.md file as the authoritative source and automatically generate every other tool-specific rule file from it. The idea matters because the alternative—hand-editing several near-identical files—invites drift, contradictions, and wasted effort.

The core issue is that the ecosystem has not standardized on one location or format for agent instructions. GitHub Copilot reads from a repository's copilot-instructions.md, Cursor historically used a .cursorrules file and later moved toward a .cursor/rules directory, Claude Code looks for CLAUDE.md, and other assistants such as Windsurf and Cline expect their own dotfiles. Each file typically contains overlapping content: how the project is structured, which frameworks and versions are used, preferred naming conventions, testing commands, and boundaries the agent should respect. When a team adopts more than one assistant, or when individual developers use different tools, these instructions must be duplicated and kept in sync manually.

AGENTS.md has emerged as a candidate for a common baseline. It is an open, Markdown-based convention intended to give coding agents a predictable place to find project guidance, and it has gained traction across several tools and vendors, including support associated with OpenAI's Codex efforts. Because it is plain Markdown with no rigid schema, it is easy to author and version alongside code. The article's argument builds on this momentum: rather than treating AGENTS.md as just one more file to maintain, treat it as the single source of truth and derive the rest.

Technically, the generation approach is straightforward. A script reads the canonical AGENTS.md and writes out the derivative files each tool expects, adjusting paths and any minor formatting differences. In many cases the transformation is little more than copying the content to the right filename, since most tools consume plain Markdown; in others, a light template or front-matter wrapper is added to match a tool's conventions, such as Cursor's rule metadata. Teams can wire this into their workflow so the generated files stay current: running it as a pre-commit hook, a package script, or a continuous integration check that fails when the derived files no longer match what AGENTS.md would produce. That CI check is important, because it turns the generated files into build artifacts rather than documents people are tempted to edit by hand.

There are trade-offs worth noting. Committing generated files keeps the repository self-contained and ensures each tool finds what it expects without extra setup, but it also adds noise to diffs and pull requests. An alternative is to generate the files locally and gitignore them, which keeps history clean at the cost of requiring every contributor to run the generation step. The single-source model also assumes the shared content is genuinely common; when a specific tool needs unique instructions, the system needs a way to append or override sections without breaking the one-way generation flow. The article's framing appears aimed at the common case, where the vast majority of guidance is identical across tools and only the filename and location differ.

This effort sits within a broader industry trend toward interoperability among AI development tools. Related moves include the Model Context Protocol, which standardizes how agents connect to external data and tools, and ongoing convergence around shared instruction formats. The underlying motivation is similar: reduce lock-in and per-tool busywork so that switching or combining assistants does not multiply maintenance. For teams evaluating the approach, the practical benefit is consistency—one edit propagates everywhere—and the main prerequisite is agreeing on AGENTS.md as the canonical file and adding a small amount of automation.

For now, the technique is likely most valuable to teams already juggling multiple assistants or anticipating that they will. Even a minimal script plus a CI guard can eliminate a recurring source of inconsistency, and because AGENTS.md is plain text, the approach carries little risk if the tooling landscape shifts again.