RLM

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.