Coordinate sub-agents to analyze large codebases without overflowing context.
npx skills add https://github.com/micaelmalta/agentic --skill rlm-micaelmalta
Please help me install this Agent Skill. Skill: rlm Source: https://github.com/micaelmalta/agentic/tree/main/skills/rlm Command: npx skills add https://github.com/micaelmalta/agentic --skill rlm-micaelmalta
Large codebases pose memory and context challenges. The RLM skill coordinates sub-agents to read, map, and synthesize insights without loading the entire project into a single context, enabling scalable analysis.
Activate the RLM workflow to scan a large repository and generate a concise summary of key findings.
High-intent search queries and answers about installing and using this skill.
To analyze a large codebase without context overflow, you can use recursive map-reduce orchestration to coordinate sub-agents that read and synthesize code in parallel, keeping the main context clear.
The best way to find deprecated API usage across a monorepo is to run a parallel mapping workflow that scans hundreds of files simultaneously and aggregates cross-file patterns into a single summary.
Yes, you can use parallel agents to extract architecture signals from multi-language projects by spawning sub-agents for selective data extraction and chunked processing, then recursively synthesizing the aggregated results.
You can process 100+ files in a repository by applying a phase-driven map-reduce workflow that indexes and filters files first, then dispatches parallel sub-agents for chunked processing without loading everything at once.
Yes, recursive codebase analysis supports both native and strict mode orchestration engines, allowing you to spawn parallel tasks and aggregate results while maintaining strict execution constraints.
Limitations of using sub-agents for large repo analysis include the need for engines that support parallel task spawning and recursive synthesis, as standard single-context processing cannot handle 100+ files without overflowing memory.
hidonguyenScout codebases to locate relevant files and surface concise file summaries.
outfitter-devAggregate scaffold test reports into unified summaries with score analysis.
martiendejongProcess large files and multi-file analyses via recursive external-context chunking.
Avi977Decompose large tasks into smaller subproblems via recursive self-invocation.
