repo-analyzer

To analyze a large monorepo efficiently, use a progressive-disclosure pipeline that starts with a lightweight tree scan and gradually reads high-signal files to map dependencies and module structure.

Progressive-disclosure repo analysis is a staged, query-driven workflow that performs tree-first discovery and selective file reads to derive dependencies, build systems, and module structure for scalable insight.

Yes, you can analyze remote Bitbucket repositories using API mode, which connects via the Bitbucket API to detect monorepos and generate per-module profiles, dependencies, and data-flow diagrams.

Repo profiling supports LOCAL mode for analyzing locally cloned codebases, outputting a consolidated repo profile with module-level sections, data-flow diagrams, and feature catalogs for easy mapping.

The best way to control analysis depth and cost is by selecting a specific tier and mode during repo profiling, which scales the progressive-disclosure reads and cache management to match your requirements.

Use a staged, cache-enabled workflow for codebase mapping when dealing with large monorepos, ensuring efficient, scalable insight by avoiding full scans and relying on cached, tiered analysis outputs.