Can I use these patterns for monorepos?

Yes, this skill provides specific patterns for placing nested AGENTS.md files in backend, frontend, and infrastructure directories to provide localized context.

What is the recommended size for a root AGENTS.md file?

It is best practice to keep the root file under 300-400 lines, using it as a high-level overview that references more detailed documentation elsewhere.

How does this help with the AI context window?

By splitting large files into modular components, agents only load the root overview and the specific subproject files relevant to their current task, saving thousands of tokens per request.

Does this work with other AI coding tools?

While optimized for Claude Code, these patterns follow the standard AGENTS.md specification supported by tools like GitHub Copilot and Cursor.

AGENTS.md Organization Patterns

Name: AGENTS.md Organization Patterns
Author: L-Sypniewski

byL-Sypniewski

学習とドキュメンテーション

Optimizes AI context window efficiency by organizing large AGENTS.md files into modular, nested structures.

概要

This skill provides standardized patterns for managing large project documentation and AI instructions within the AGENTS.md framework. By breaking down monolithic instruction files into modular references and nested subproject configurations, it significantly reduces context window usage—often by 50-79%—while ensuring that Claude and other AI agents receive highly relevant, localized guidance tailored to their specific workspace directory. It is an essential toolkit for developers managing complex codebases who want to improve AI performance and maintainability.

主な機能

Modular file referencing for detailed coding and testing standards
Context window optimization through on-demand information loading
Structured migration strategies for refactoring monolithic documentation
0 GitHub stars
Nested AGENTS.md support for monorepos and subproject isolation
Ready-to-use templates for root and specialized subproject organization

ユースケース

Scaling documentation in large monorepos with mixed technology stacks
Reducing token costs and performance lag in long-running AI coding sessions
Maintaining clear separation between high-level architecture and low-level implementation details

概要

主な機能

Modular file referencing for detailed coding and testing standards
Context window optimization through on-demand information loading
Structured migration strategies for refactoring monolithic documentation
0 GitHub stars
Nested AGENTS.md support for monorepos and subproject isolation
Ready-to-use templates for root and specialized subproject organization

ユースケース

Scaling documentation in large monorepos with mixed technology stacks
Reducing token costs and performance lag in long-running AI coding sessions
Maintaining clear separation between high-level architecture and low-level implementation details