| name | rlm |
|---|---|
| description | 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). |
| allowed-tools | ["Read", "Write", "Edit", "Grep", "Glob", "Bash"] |
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 likellm_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
-
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 -
Scout the context quickly
bash 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)))" -
Choose a chunking strategy - Prefer semantic chunking if the format is clear (markdown headings, JSON objects, log timestamps). - Otherwise, chunk by characters (size around chunk_chars, optional overlap).
-
Materialise chunks as files (so subagents can read them)
bash python3 .claude/skills/rlm/scripts/rlm_repl.py exec <<'PY' paths = write_chunks('.claude/rlm_state/chunks', size=200000, overlap=0) print(len(paths)) print(paths[:5]) PY -
Subcall loop (delegate to rlm-subcall) - For each chunk file, invoke the rlm-subcall subagent with:
- the user query,
- the chunk file path,
- and any specific extraction instructions.
- Keep subagent outputs compact and structured (JSON preferred).
- Append each subagent result to buffers (either manually in chat, or by pasting into a REPL add_buffer(...) call).
-
Synthesis - Once enough evidence is collected, synthesise the final answer in the main conversation. - Optionally ask rlm-subcall once more to merge the collected buffers into a coherent draft.
Guardrails
- Do not paste large raw chunks into the main chat context.
- Use the REPL to locate exact excerpts; quote only what you need.
- Subagents cannot spawn other subagents. Any orchestration stays in the main conversation.
- Keep scratch/state files under .claude/rlm_state/.