Optimizes project structure and documentation layers to maximize AI comprehension and coding accuracy.
This skill implements a specialized architectural framework designed to enhance the synergy between human developers and AI assistants like Claude. By establishing explicit semantic boundaries, multi-level documentation layers (L1-L3), and utilizing the .ai-context.yaml configuration, it ensures that AI models have the necessary context without overwhelming their token limits. This approach reduces hallucinations, improves code navigation, and makes complex repositories more discoverable for automated analysis and development tasks.
주요 기능
01Automated generation of .ai-context.yaml for semantic project mapping
02Standardized module headers for immediate entry-point clarification
033 GitHub stars
04Anti-pattern detection to identify AI-confusing structures like circular dependencies
05Layered documentation strategy to optimize token budget and context depth
06Context priority guidelines to rank the most relevant files for specific tasks
사용 사례
01Reducing token costs by implementing L1/L2 documentation layers for large modules
02Standardizing a complex monorepo for more reliable AI-assisted refactoring
03Onboarding Claude Code to a new project with optimized discovery paths
