Execute recursive language model loops for long-context tasks via a local Python REPL.
npx skills add https://github.com/brainqub3/brainqub3 --skill rlm-brainqub3
Please help me install this Agent Skill. Skill: rlm Source: https://github.com/brainqub3/brainqub3/tree/main/.claude/skills/rlm Command: npx skills add https://github.com/brainqub3/brainqub3 --skill rlm-brainqub3
This Skill tackles the challenge of processing extremely large text files that exceed standard LLM context window limits, enabling iterative analysis and extraction.
Use the rlm skill to find all occurrences of 'error code 500' within the log file '/var/log/app.log'.
High-intent search queries and answers about installing and using this skill.
To process large text files that exceed context window limits, this Skill chunks the input and iteratively analyzes each segment using a dedicated subagent. It leverages a persistent local Python REPL to manage the workflow, enabling detailed extraction from documents too big for direct LLM input.
Recursive context chunking works by breaking down massive documents into smaller, manageable pieces for long context analysis. This Skill executes a Recursive Language Model-style loop, offloading each chunk to a subagent for LLM queries, then iteratively processing the results to synthesize detailed findings.
Yes, you need Python 3 installed to run iterative LLM analysis on large documents. This Skill relies on a persistent local Python REPL to manage the chunking and recursive processing loop, and it also requires a defined sub-LLM interface for delegating chunk-level queries.
Yes, you can search for specific error codes in a multi-hundred-page technical manual. By iteratively chunking the large file and delegating searches to a subagent, the Skill enables step-by-step inspection and extraction of specific configuration details or troubleshooting steps beyond standard context windows.
The best way to analyze extensive log files with limited context windows is using a recursive loop with a subagent. This approach manages large context files by chunking and iterative processing, offloading chunk-level processing to a dedicated subagent to extract targeted information without hitting token limits.
jdubrayCoordinates a persistent Python REPL and subagent to analyze long documents in chunks.
ShihaoZhou-NEUAggregate Web3 market data and tokenomics into structured research reports.
nigo81Build a local vector knowledge base with semantic search and reranking.
Suntory-N-WaterExecute Serena MCP tools as TypeScript code to reduce token consumption.
