How do I install the Rlm Distill Agent skill?

Run npx skillmds@latest add richfrem/rlm-distill-agent-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Rlm Distill Agent safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Rlm Distill Agent?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Rlm Distill Agent free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Rlm Distill Agent?

richfrem (@richfrem) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rlm Distill Agent by richfrem — Agent Skill

Name: Rlm Distill Agent
Author: richfrem

Rlm Distill Agent

Distills uncached files into the Recursive Language Model(RLM) Summary cache Ledger. You (the agent) ARE the distillation engine. Read each file deeply, write a high-quality 1-sentence summary, inject it via inject_summary.py. The purpose is if you read the full file once and produce a great summary once it will avoid the need to read the file every time you need to know what the script does or what the details of the file are. most cases the RLM summary should be sufficient. Use when files are missing from the ledger and need to be summarized. <example> user: "Summarize these new plugin files into the RLM ledger" assistant: "I'll use rlm-distill-agent to read and summarize each file into the cache." </example> <example> user: "The RLM ledger is missing 40 files -- fill the gaps" assistant: "I'll use rlm-distill-agent to process the missing files." </example>

richfrem Updated Sep 21, 2026

File contents

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.

---name: rlm-distill-agent-2description: "Distills uncached files into the Recursive Language Model(RLM) Summary cache Ledger. You (the agent) ARE the distillation engine. Read each file deeply, write a high-quality 1-sentence summary, inject it via inject_summary.py. The purpose is if you read the full file once and produce a great summary once it will avoid the need to read the file every time you need to know what the script does or what the details of the file are. most cases the RLM summary should be sufficient. Use when files are missing from the ledger and need to be summarized. <example> user: \"Summarize these new plugin files into the RLM ledger\" assistant: \"I'll use rlm-distill-agent to read and summarize each file into the cache.\" </example> <example> user: \"The RLM ledger is missing 40 files -- fill the gaps\" assistant: \"I'll use rlm-distill-agent to process the missing files.\" </example>"---# RLM Distill Agent## RoleYou ARE the distillation engine. You replace the local Ollama `distiller.py` script with your ownsuperior intelligence. Read each uncached file deeply, write an exceptionally good 1-sentencesummary, 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:```bash# See: ../../SKILL.md# Creates rlm_profiles.json, manifest, and empty cache```## Execution Protocol### 1. Identify missing files```bashpython3 ./scripts/inventory.py --profile projectpython3 ./scripts/inventory.py --profile tools```### 2. For each missing file -- read deeply and write a great summaryRead 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```bashpython3 ./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 missingDo not attempt manual distillation for large batches. Choose an engine based on cost andthroughput, 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.

Frequently asked questions