Does this skill work with any programming language?

Yes, the AI-Friendly Architecture standards are language-agnostic and can be applied to web apps, APIs, libraries, or monorepos in any language.

What are the documentation layers (L1, L2, L3)?

L1 is a Quick Reference (<500 tokens), L2 is Detailed API documentation (<5000 tokens), and L3 includes complete implementation examples and edge cases.

What is AI-Friendly Architecture?

It is a design methodology that uses explicit patterns, layered documentation, and semantic boundaries to make a codebase more discoverable and understandable for AI models.

How does .ai-context.yaml help Claude?

It provides a machine-readable map of your project, including entry points, module priorities, and dependencies, allowing the AI to load the most relevant context for any task.

Can I retrofit an existing project with this skill?

Absolutely. The skill includes a quick-start guide to implement basic AI context in under an hour without changing your functional code.

AI-Friendly Architecture Guide

Name: AI-Friendly Architecture Guide
Author: AsiaOstrich

byAsiaOstrich

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학습 및 문서화

Optimizes project structures and documentation layers to maximize AI collaboration efficiency and analysis accuracy.

This skill provides a standardized framework for designing software architecture specifically optimized for AI interaction. By implementing layered documentation (L1-L3), explicit semantic boundaries, and the .ai-context.yaml configuration, it helps Claude navigate complex codebases with high precision. This approach reduces token consumption, eliminates architectural guesswork, and prevents common AI pitfalls like circular dependencies and context overflow, ensuring more reliable code generation and refactoring.

주요 기능

01Semantic boundary definitions to isolate AI analysis and prevent context leakage

02Standardized .ai-context.yaml for explicit module mapping and dependency tracking

03Automated detection and scaffolding of AI-friendly project structures

04Multi-level context layering (Quick Ref, Detailed, Examples) to optimize token budgets

05Token-efficient YAML-based architectural headers for source files

0640 GitHub stars

사용 사례

01Structuring new repositories to be 'AI-native' from the first commit

02Optimizing legacy codebases for better analysis and refactoring by AI assistants

03Standardizing team-wide documentation patterns for seamless AI-human collaboration

주요 기능

01Semantic boundary definitions to isolate AI analysis and prevent context leakage

02Standardized .ai-context.yaml for explicit module mapping and dependency tracking

03Automated detection and scaffolding of AI-friendly project structures

04Multi-level context layering (Quick Ref, Detailed, Examples) to optimize token budgets

05Token-efficient YAML-based architectural headers for source files

0640 GitHub stars

사용 사례

01Structuring new repositories to be 'AI-native' from the first commit

02Optimizing legacy codebases for better analysis and refactoring by AI assistants

03Standardizing team-wide documentation patterns for seamless AI-human collaboration