Adopt a framework for designing skills as modular building blocks to ensure predictable token usage and a maintainable, scalable architecture. This skill helps break down large, monolithic skills into focused, single-responsibility components that are easier to test, maintain, and optimize. By implementing progressive disclosure principles and providing tools for analysis, token estimation, and validation, it enables the creation of efficient, high-quality skills that grow without becoming unwieldy.
Key Features
01Estimates token consumption to optimize for context window efficiency
02Analyzes skill complexity and recommends modularization strategies
03Validates module structure and compliance with best practices
04Includes detailed implementation patterns and migration guides
05Provides a framework and design principles for modular skill architecture
062 GitHub stars
Use Cases
01Architecting new, complex skills for long-term maintainability
02Refactoring large, monolithic skills into focused, reusable components
03Optimizing skill design to reduce token consumption and improve performance
