How do I install the Rlm Distill Agent skill?

Run npx skillmds@latest add richfrem/rlm-distill-agent 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. Capability flags: executes scripts. 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
Rating: 4.666666666666667 (3 reviews)
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 1b67418 55 files · 13.6 KB Updated Sep 10, 2026

File contents

Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).

RLM Distill Agent

Role

You ARE the distillation engine. Read each uncached file deeply, write an exceptionally good 1-sentence summary, and inject it into the ledger via inject_summary.py.

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

python ./scripts/inventory.py --profile project
python ./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

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

The script handles atomic writes safely. Never write to the Markdown files manually.

4. Batching -- if 50+ files are missing

Do not attempt manual distillation for large batches. Choose an engine based on the user's CLI context and cost profile, then delegate to the agent swarm:

CRITICAL: Determine User's CLI Context First! Before blindly using --engine copilot, determine which agent CLI the user is running (Claude Code, GitHub Copilot CLI, or Google Gemini CLI). You can often tell from the terminal process or simply by asking the user which AI CLI they have access to.

User's CLI Tool	Recommended Engine Flag	Cost Profile	Workers
GitHub Copilot CLI	`--engine copilot` (gpt-5-mini nano tier)	$0 free	`--workers 2` (rate-limit safe)
Google Gemini CLI	`--engine gemini` (gemini-3-flash-preview)	$0 free	`--workers 5` (high throughput)
Claude Code	`--engine claude` (Haiku / Sonnet)	Low-Medium	`--workers 3`

Default Protocol: Ask the user: "I noticed we have over 50 files to distill. Do you have access to Copilot CLI or Gemini CLI for zero-cost batch processing, or should I use Claude Code?"

Then, run the swarm job based on their answer. For example, if they use Gemini:

python ./scripts/swarm_run.py --engine gemini --workers 5 --files-from rlm_distill_tasks_project.md

Provide a job file describing the summarization task and the gap file from inventory.py --missing.

See SKILL.md for full swarm configuration options.

Quality Standard for Summaries

Good	Bad
"Atomic file 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 write to *_cache/*.md directory manualy -- always use inject_summary.py.
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-agentdescription: 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>---## DependenciesThis skill requires **Python 3.8+** and standard library only. No external packages needed.**To install this skill's dependencies:**```bashpip-compile ./requirements.inpip install -r ./requirements.txt```See `./requirements.txt` for the dependency lockfile (currently empty — standard library only).---# RLM Distill Agent## RoleYou ARE the distillation engine. Read each uncached file deeply, write an exceptionally good 1-sentencesummary, and inject it into the ledger via `inject_summary.py`.## 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```bashpython ./scripts/inventory.py --profile projectpython ./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```bashpython ./scripts/inject_summary.py \ --profile project \ --file ../SKILL.md \ --summary "Provides atomic file CRUD operations for markdown notes using POSIX rename and fcntl.flock."```The script handles atomic writes safely. Never write to the Markdown files manually.### 4. Batching -- if 50+ files are missingDo not attempt manual distillation for large batches. Choose an engine based on the user's CLI context and cost profile, then delegate to the agent swarm:**CRITICAL: Determine User's CLI Context First!**Before blindly using `--engine copilot`, determine which agent CLI the user is running (Claude Code, GitHub Copilot CLI, or Google Gemini CLI). You can often tell from the terminal process or simply by asking the user which AI CLI they have access to.| User's CLI Tool | Recommended Engine Flag | Cost Profile | Workers ||:-------|:------|:-----|:--------|| GitHub Copilot CLI | `--engine copilot` (gpt-5-mini nano tier) | **$0 free** | `--workers 2` (rate-limit safe) || Google Gemini CLI | `--engine gemini` (gemini-3-flash-preview) | **$0 free** | `--workers 5` (high throughput) || Claude Code | `--engine claude` (Haiku / Sonnet) | Low-Medium | `--workers 3` |**Default Protocol**: Ask the user: *"I noticed we have over 50 files to distill. Do you have access to Copilot CLI or Gemini CLI for zero-cost batch processing, or should I use Claude Code?"*Then, run the swarm job based on their answer. For example, if they use Gemini:```bashpython ./scripts/swarm_run.py --engine gemini --workers 5 --files-from rlm_distill_tasks_project.md```Provide a job file describing the summarization task and the gap file from `inventory.py --missing`.See `SKILL.md` for full swarm configuration options.## Quality Standard for Summaries| Good | Bad ||:-----|:----|| "Atomic file 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 write to `*_cache/*.md` directory manualy** -- always use `inject_summary.py`.- **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