---

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

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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

   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

   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)))"
   

3. 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).

4. Materialise chunks as files (so subagents can read them)

   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
   

5. 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).

6. 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/.