Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/richfrem/agent-plugins-skillsnpx agentmods add skills/richfrem/agent-plugins-skills/rlm-distill-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/richfrem/agent-plugins-skills/rlm-distill-agent)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/rlm-distill-agent"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/rlm-distill-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/rlm-distill-agent"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/rlm-distill-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00208 | $0.01160 |
| Opus 5 | $0.00104 | $0.00580 |
| Sonnet 5 | $0.00042 | $0.00232 |
| Haiku 4.5 | $0.00021 | $0.00116 |
Grade A, and why
rlm-distill-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
48 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- acceptance-criteria.md 298 B
- assets/diagrams/rlm_late_binding_flow.mmd 53 B
- assets/diagrams/rlm_tool_enrichment_flow.mmd 56 B
- assets/resources/distiller_manifest.json 52 B
- assets/resources/jobs/distill_wiki.job.md 49 B
- assets/resources/manifest-index.json 48 B
- assets/resources/prompts/rlm/rlm_summarize_general.md 71 B
- assets/resources/prompts/rlm/rlm_summarize_tool.md 68 B
- assets/resources/rlm_manifest.json 46 B
- evals/evals.json 1004 B
- evals/results.tsv 172 B
- references/acceptance-criteria.md 42 B
- references/BLUEPRINT.md 32 B
- references/cheapest_models.json 40 B
- references/cheapest_models.md 38 B
- references/diagrams/distillation_process.mmd 56 B
- references/diagrams/logic.mmd 41 B
- references/diagrams/rlm_late_binding_flow.mmd 57 B
- references/diagrams/rlm_mechanism_workflow.mmd 58 B
- references/diagrams/rlm_mechanism_workflow.png 58 B
- references/diagrams/rlm_tool_enrichment_flow.mmd 60 B
- references/diagrams/rlm-factory-architecture.mmd 60 B
- references/diagrams/rlm-factory-architecture.png 60 B
- references/diagrams/rlm-factory-dual-path.mmd 57 B
- references/diagrams/rlm-factory-dual-path.png 57 B
- references/diagrams/rlm-factory-workflow.mmd 56 B
- references/diagrams/search_process.mmd 50 B
- references/diagrams/unpacking.mmd 45 B
- references/diagrams/workflow.mmd 44 B
- references/examples/rlm_profiles.json 49 B
- references/examples/rlm_summary_cache_manifest.json 63 B
- references/examples/rlm_tools_manifest.json 55 B
- references/gap_analysis_rlm_v1.md 42 B
- references/prompt.md 29 B
- references/research-summary.md 39 B
- references/research/2512.24601v1.pdf 48 B
- references/research/summary.md 42 B
- references/RLM_ARCHITECTURE.md 39 B
- requirements.txt 22 B
- scripts/check_cached.py 32 B runs code
- scripts/cleanup_cache.py 33 B runs code
- scripts/distill_one.py 31 B runs code
- scripts/distiller.py 29 B runs code
- scripts/inject_summary.py 34 B runs code
- scripts/inventory.py 29 B runs code
- scripts/query_cache.py 31 B runs code
- scripts/rlm_config.py 30 B runs code
- scripts/swarm_run.py 29 B runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 118 lines · 208 tokens per session scan A 79a65048dd94
rlm-distill-agent is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed today), licensed MIT. It adds 208 tokens to every session and 1,160 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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