Rlm

rlm (Recursive Language Model workflow)

Use this skill when:

• The user provides a large context file or document directory.
• You need iterative search/chunk/extract over that context.
• You want to reuse loaded context across multiple queries.

Inputs

Required:

• context=<path>: file path (single-file mode) or directory path (corpus mode)
• query=<question>: question/task to run against the loaded context

Optional:

• chunk_chars=<int> (default ~200000)
• overlap_chars=<int> (default 0)
• strict=true (corpus mode only, fail on first parse error)

If arguments are missing, ask for:

1. context path
2. query

Resolve <skill-dir> to the directory containing this SKILL.md before running bundled scripts. Do not assume the repository working directory contains scripts/rlm_repl.py.

Workflow

1. Initialize state.

   Single-file mode:

   python3 <skill-dir>/scripts/rlm_repl.py init <context_path>
   python3 <skill-dir>/scripts/rlm_repl.py status
   

   Corpus mode (recursive, honors .rlmignore if present):

   python3 <skill-dir>/scripts/rlm_repl.py init-corpus <context_dir>
   python3 <skill-dir>/scripts/rlm_repl.py status
   

   Corpus strict mode:

   python3 <skill-dir>/scripts/rlm_repl.py init-corpus <context_dir> --strict
   

2. Check/install optional parsers when needed.

   python3 <skill-dir>/scripts/rlm_repl.py check-deps
   python3 <skill-dir>/scripts/rlm_repl.py install-deps --all --dry-run
   

   Install missing dependencies only with user approval.

3. Scout the loaded context.

   python3 <skill-dir>/scripts/rlm_repl.py exec -c "print(peek(0, 3000))"
   python3 <skill-dir>/scripts/rlm_repl.py exec -c "print(peek(len(content)-3000, len(content)))"
   

4. Materialize chunks for subagent analysis.

   python3 <skill-dir>/scripts/rlm_repl.py exec <<'PY'
   paths = write_chunks('.mnemonic/rlm_state/chunks', size=200000, overlap=0)
   print(len(paths))
   print(paths[:5])
   PY
   

5. Use the host's available subagent mechanism for chunk analysis when useful, then synthesize results. If subagents are unavailable, analyze chunks sequentially.

Guardrails

• Do not paste large raw chunks into chat.
• Quote only needed excerpts.
• Keep scratch/state files under .mnemonic/rlm_state/.
• For first iteration, refresh manually when sources change:
  • run python3 <skill-dir>/scripts/rlm_repl.py reset
  • then reinvoke the skill with context=... query=...

Notes

• Optional document parsers:
  • PDF: pypdf
  • DOCX: python-docx
  • ODT: odfpy
• Default corpus excludes: .git/, node_modules/, bin/, _archive/.
• Additional reference docs are in references/.