rlm

rlm is a Context window & token budget skill for AI agents, published by brainqub3 in the RLM repository on GitHub. Skillspace rates rlm D (68/100) and it passed the safety scan; the rating is based on static analysis only.

Recursive Language Model (RLM) loop for processing a context that is too large to read into the conversation directly. Loads the context as a variable in a persistent Python REPL and answers the query by writing code that probes, chunks, and programmatically sub-queries a cheap LLM (`llm_query`) over slices of it, then aggregates. Use this WHENEVER the user points you at a big context file/log/transcript/codebase/scraped corpus (anything from ~50K chars up to millions) and asks a question that needs most of the content -- counting, aggregating, classifying every item, multi-hop lookup, or summarising the whole thing -- ESPECIALLY when the answer "depends on almost every line" and a single retrieval/grep won't do. Trigger it even if the user doesn't say "RLM": phrases like "this file is huge", "go through the whole log", "how many X across all of these", "label every row", or "it won't fit in context" are all signals to use this skill. Prefer it over dumping the file into chat.

Grade

D

Quality

68 / 100

Safety

Passed

GitHub stars

392

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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