synthetic-dataRoom

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.

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 leaf 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.

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.