ai-friendly-module-identifier

To identify all modules in a codebase for architecture analysis, scan the repository to detect boundaries across directory-level and file-level granularities. This produces a structured list of module paths, names, depths, and per-file line counts.

Dual-level module boundary scanning is the process of detecting module structures at both directory-level and file-level granularities. It maps business, data persistence, API adaptation, and infrastructure layers to clarify structural dependencies.

Codebase modularity analysis works for mapping API adaptation and data persistence layers by scanning the repository to identify structural boundaries. It outputs relative paths and depth metrics to support architecture reviews across these layers.

To extract module paths and file line counts for a refactoring plan, scan the codebase to identify module boundaries at dual granularities. This generates structured architecture-aware reports detailing relative paths, depths, and line counts.

The best way to scan a repository to understand module dependencies is to perform dual-level boundary identification across directory and file structures. This yields structured results mapping module paths and depths for downstream tooling.

You need to map module boundaries at file-level granularity when assessing modularity and understanding dependencies within specific implementation layers. It captures per-file line counts and structural depth for detailed architecture review.