improve-codebase-architecture

To identify shallow modules requiring refactoring, you need to surface architectural friction and deepening opportunities. This process reveals seams that lack deep interfaces, guiding you toward consolidating tightly coupled modules for better testability and AI-navigability.

Architectural deepening is the process of turning shallow modules into deep ones by proposing concrete refactor opportunities. It improves testability and AI-navigability by grounding structural changes in domain language from docs/CONTEXT.md and ADRs to resolve architectural friction.

To consolidate tightly coupled modules and make a codebase more AI-navigable, analyze the repository to surface deepening opportunities. This proposes actionable refactors that align with LANGUAGE.md vocabulary, transforming shallow interfaces into deep, accessible architectural seams.

Architectural refactor guidance relies heavily on domain-language terminology from docs/CONTEXT.md and ADRs to encode deepening candidates. Without these files, the analysis lacks project-specific vocabulary alignment and contextual grounding for accurate friction surfacing.

The best way to align refactoring opportunities with domain language is to encode deepening candidates using terminology from docs/CONTEXT.md and ADRs. This ensures proposed architectural improvements match the project's LANGUAGE.md vocabulary for accurate friction surfacing.

You should avoid automated architectural friction analysis if your repository lacks foundational domain context files like docs/CONTEXT.md, ADRs, or a LANGUAGE.md. Without these vocabulary sources, the tool cannot accurately encode or propose context-aware deepening opportunities.