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/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)

   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
   

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/.