Process large text and code files exceeding LLM context windows via recursive chunking and sub-agent delegation.
npx skills add https://github.com/legout/pi-config --skill rlm-legout
Please help me install this Agent Skill. Skill: rlm Source: https://github.com/legout/pi-config/tree/main/skills/rlm Command: npx skills add https://github.com/legout/pi-config --skill rlm-legout
This Skill solves the problem of analyzing extremely large documents, logs, or codebases that exceed the standard LLM context window, preventing information loss and truncation errors.
Use the rlm skill to analyze the large log file located at path/to/server.log and answer the user query about connection errors.
High-intent search queries and answers about installing and using this skill.
To analyze large log files exceeding the LLM context window, use recursive chunking to break massive files into manageable chunks and synthesize results using sub-LLM agents. This prevents information loss and truncation errors during large-scale text processing.
The best way to process massive codebases without losing context is recursive chunking with handle-based search. This method uses token-saving stubs for efficient exploration and parallel sub-LLM invocation to ensure accurate synthesis of large datasets.
Smart chunking works by automatically detecting content types like Markdown, JSON, and Code to split files at logical boundaries. This content-aware splitting avoids arbitrary character counts and maintains structural integrity during analysis.
Yes, you can search specific sections of massive documentation without loading full content by using handle-based search. This technique allows efficient exploration of large datasets using token-saving stubs instead of loading the entire file into the context window.
The limitations of using sub-agents for large file analysis include the complexity of maintaining persistent state across recursive chunks. While parallel sub-LLM invocation synthesizes large datasets accurately, managing context boundaries requires content-aware splitting to avoid logical errors.
brainqub3Execute recursive language model loops for long-context tasks via a local Python REPL.
kfchouQuery personal wikis to synthesize answers from multiple markdown sources.
trohitgCoordinate sub-agent delegation and track task completion events across multi-agent workflows.
richardwhiteiiLoad large text outside the prompt, chunk it, and run batched sub-queries with aggregation.
