RLM Distill Agent

Role

You ARE the distillation engine. You replace the local Ollama distiller.py script with your own superior intelligence. Read each uncached file deeply, write an exceptionally good 1-sentence summary, and inject it into the ledger via inject_summary.py .

Never run distiller.py . You are faster, smarter, and don't require Ollama to be running.

When to Use

• Files are missing from the ledger (as reported by inventory.py )

• A new plugin, skill, or document was just created

• A file's content changed significantly since it was last summarized

Prerequisites

First-time setup or missing profile? Run the rlm-init skill first:

See: ../../SKILL.md

Creates rlm_profiles.json, manifest, and empty cache

Execution Protocol

1. Identify missing files

python3 ./scripts/inventory.py --profile project python3 ./scripts/inventory.py --profile tools

2. For each missing file -- read deeply and write a great summary

Read the entire file with view_file . Do not skim.

A great RLM summary answers: "What does this file do, what problem does it solve, and what are its key components/functions?" in one dense sentence.

3. Inject the summary

python3 ./scripts/inject_summary.py
--profile project
--file ../../SKILL.md
--summary "Provides atomic vault CRUD operations for Obsidian notes using POSIX rename and fcntl.flock."

The script uses fcntl.flock for safe concurrent writes. Never write to the JSON directly.

4. Batching -- if 50+ files are missing

Do not attempt manual distillation for large batches. Choose an engine based on cost and throughput, then delegate to the agent swarm:

Engine Model Cost Workers Best For

--engine copilot

gpt-5-mini (nano tier) $0 free --workers 2 (rate-limit safe) Bulk summarization, zero-cost default

--engine gemini

gemini-3-pro-preview $0 free --workers 5

Large-context batches, higher throughput

--engine claude

Haiku / Sonnet Low-Medium --workers 3

Higher quality summaries, not free

Local Ollama granite3.2:8b $0 (CPU) 1 (serial) Offline / air-gapped only

Default recommendation: start with --engine copilot (free, no rate risk at workers=2). Switch to --engine gemini --workers 5 if you need faster throughput.

Delegate to the agent-loops:agent-swarm skill with the appropriate engine and job:

Engine Model Cost Workers Best For

copilot

gpt-5-mini (nano tier) $0 free 2 (rate-limit safe) Bulk summarization, zero-cost default

gemini

gemini-3-pro-preview $0 free 5 Large-context batches, higher throughput

claude

Haiku / Sonnet Low-Medium 3 Higher quality summaries, not free

Provide the job file: ../../resources/jobs/rlm_chronicle.job.md and the gap file from inventory.py --missing .

See ../../SKILL.md for full swarm configuration options.

Quality Standard for Summaries

Good Bad

"Atomic vault CRUD using POSIX rename + flock, preserving YAML frontmatter via ruamel.yaml." "This file handles file operations."

"3-phase search skill: RLM ledger -> ChromaDB -> grep, escalating from O(1) to exact match." "Searches for things in the codebase."

Rules

• Never run distiller.py -- it calls Ollama, which is slow and may not be running.

• Never write to *_cache.json directly -- always use inject_summary.py (uses fcntl.flock ).

• Read the whole file -- skimming produces summaries that miss key details.

• Source Transparency Declaration: list which files you summarized and their injected summaries.