rlm
Run a Recursive Language Model-style loop for long-context tasks. Uses a persistent local Python REPL and an rlm-subcall subagent as the sub-LLM (llm_query).
npx skills add majiayu000/claude-skill-registry --skill rlm-brainqub3-brainqub3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# rlm (Recursive Language Model workflow) Use this Skill when: - The user provides (or references) a very large context file (docs, logs, transcripts, scraped webpages) that won't fit comfortably in chat context. - You need to iteratively inspect, search, chunk, and extract information from that context. - You can delegate chunk-level analysis to a subagent. ## Mental model - Main Claude Code conversation = the root LM. - Persistent Python REPL (`rlm_repl.py`) = the external environment. - Subagent `rlm-subcall` = the sub-LM used like `llm_query`. ## How to run ### Inputs This Skill reads `$ARGUMENTS`. Accept these patterns: - `context=<path>` (required): path to the file containing the large context. - `query=<question>` (required): what the user wants. - Optional: `chunk_chars=<int>` (default ~200000) and `overlap_chars=<int>` (default 0). If the user didn't supply arguments, ask for: 1) the context file path, and 2) the query. ### Step-by-step procedure 1. Initialise the REPL state ```bash python3 .claude/skills/rlm/scripts/rlm_repl.py init <context_path> python3 .claude/skills/rlm/scripts/rlm_repl.py status ``` 2. Scout the context quickly ```bash python3 .claude/skills/rlm/scr
- Mental model
- How to run
- Inputs
- Step-by-step procedure
- Guardrails
python3 .claude/skills/rlm/scripts/rlm_repl.py init <context_path> python3 .claude/skills/rlm/scripts/rlm_repl.py status python3 .claude/skills/rlm/scripts/rlm_repl.py exec -c "print(peek(0, 3000))" python3 .claude/skills/rlm/scripts/rlm_repl.py exec -c "print(peek(len(content)-3000, len(content)))" python3 .claude/skills/rlm/scripts/rlm_repl.py exec <<'PY'
What does the rlm skill do?
Run a Recursive Language Model-style loop for long-context tasks. Uses a persistent local Python REPL and an rlm-subcall subagent as the sub-LLM (llm_query).
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rlm-brainqub3-brainqub3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.