Agent-Skills.md

Agent Skills: rlm (Recursive Language Model workflow)

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).

UncategorizedID: jdubray/puffin/rlm

Repository

jdubray/puffin
jdubrayLicense: MIT
245

Install this agent skill to your local

pnpm dlx add-skill https://github.com/jdubray/puffin/tree/HEAD/.claude/skills/rlm

Skill Files

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.claude/skills/rlm/SKILL.md

Skill Metadata

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).

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/.