improve-codebase-architecture

You identify shallow modules by analyzing module interfaces, dependencies, and boundary interactions to surface friction points and tight couplings. This targets evolving architectures to improve testability and maintainability.

Improving codebase architecture for AI navigability involves deepening shallow modules to reduce friction across interfaces. It categorizes dependencies and presents candidate refactor clusters to make modules easier for automated tools to parse.

Finding refactor targets in a codebase with tight couplings requires analyzing boundary interactions and dependency categories. This surfaces candidate clusters with context on involved modules and testing impact to guide structural improvements.

Yes, you can generate RFC-ready candidates from a codebase architecture analysis. The workflow outputs candidate refactor clusters with detailed context on involved modules, coupling, and dependency categories intended directly for RFC creation.

Yes, this codebase refactoring approach works for evolving architectures by surfacing deepening opportunities across modules and interfaces. It defines a structured workflow emphasizing boundary-based testing while avoiding premature interface changes.

You should avoid premature interface changes during codebase refactoring to emphasize boundary-based testing and ensure structural stability. Deepening shallow modules requires analyzing dependency categories and coupling impacts before modifying boundaries.