Coordinate parallel agents to analyze large codebases via filesystem indexing and map-reduce pipelines.
npx skills add https://github.com/BowTiedSwan/rlm-skill --skill rlm-bowtiedswan
Please help me install this Agent Skill. Skill: rlm Source: https://github.com/BowTiedSwan/rlm-skill/tree/main Command: npx skills add https://github.com/BowTiedSwan/rlm-skill --skill rlm-bowtiedswan
This Skill enables fast analysis of very large codebases by treating the filesystem as a database and orchestrating multiple agents to read, map, and reduce insights without degrading context.
Ask Claude to enable the RLM skill and start analyzing a large repository for architecture or security insights.
High-intent search queries and answers about installing and using this skill.
Large codebase analysis can be performed by coordinating parallel agents to read and process files in isolation, using a map-reduce pipeline to yield structured insights without degrading context.
Finding security vulnerabilities across a massive repository is best handled by orchestrating background agents to map and reduce insights from millions of lines of code without overwhelming the active context.
Yes, you can analyze codebases with millions of lines of code by treating the filesystem as an external database and adapting between Native Mode for fast traversal and Strict Mode for dense data processing.
To start analyzing repository architecture, ask Claude to enable the RLM skill, which runs an Index, Filter, Map, and Reduce pipeline to produce final architectural insights from your files.
Dense data processing on large repos requires an engine like rlm.py for Strict Mode, whereas fast traversal uses standard tools like grep or find for Native Mode without extra dependencies.
Use Native Mode for speedy filesystem traversal across large repos, and switch to Strict Mode when you need dense data processing to identify complex patterns or code smells across files.
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