Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dmoskov/sharedskills/rlmnpx skills add dmoskov/sharedskills --skill rlmgit clone --depth 1 https://github.com/dmoskov/sharedskillsWhat 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 | $0.00049 | $0.00980 |
| Opus 5 | $0.00024 | $0.00490 |
| Sonnet 5 | $0.00010 | $0.00196 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
rlm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recursive Language Model (RLM)
You have access to rlm-query and rlm-batch for delegating long-context work to sub-agents. Do not try to orchestrate the work yourself — your job is to set up the workspace and immediately hand off to a depth-0 orchestrator.
Setup
SKILL_DIR="$(dirname "$(readlink -f ~/.claude/skills/rlm/SKILL.md 2>/dev/null || echo ~/.claude/skills/rlm/SKILL.md)")"
export PATH="${SKILL_DIR}/scripts:$PATH"
What to do
-
Write a prompt file describing what needs to be done, including paths to any input files:
TASK="my-task" mkdir -p .rlm/$TASK cat > .rlm/$TASK/task.md << 'EOF' Analyze /path/to/big-document.txt for liability risks... EOF -
Launch the root orchestrator in the background so you can give the user progress updates while it runs:
rlm-query .rlm/$TASK/task.md .rlm/$TASK/result.out \ --task $TASK --model opus --max-depth 2 & RLM_PID=$! -
Poll for progress and keep the user informed. Check active sub-agents, completed results, and whether the orchestrator has finished:
# How many sub-agents are running? tmux list-sessions 2>/dev/null | grep -c "rlm-$TASK" || echo 0 # How many results are in so far? ls .rlm/$TASK/results/*.out 2>/dev/null | wc -l # Is the orchestrator done? kill -0 $RLM_PID 2>/dev/null && echo "still running" || echo "done"Tell the user things like "3 sub-agents active, 5/16 chunks processed so far" while waiting.
-
Read the result once the orchestrator finishes:
wait $RLM_PID cat .rlm/$TASK/result.out
The orchestrator receives the full RLM instructions (rlm-agent.md) automatically and knows how to split, delegate to sub-agents, and aggregate. You don't need to read those instructions yourself.
Configuration
Set these via flags on rlm-query or environment variables. Choose based on the task:
--model/RLM_MODEL(default:opus) — Model for the orchestrator and sub-agents. Useopusfor the root orchestrator (it needs to plan and decompose). Sub-agents doing straightforward work (classification, extraction) can usesonnet— the orchestrator can pass--model sonnetwhen it callsrlm-batch.
What ships with it
3 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.
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.
- 2d ago First seen · 80 lines · 49 tokens per session scan A 252505aa28fa
rlm is a skill published in the GitHub repository dmoskov/sharedskills (2 stars, last pushed 11d ago), licensed MIT. It adds 49 tokens to every session and 980 once invoked, about $0.0002 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 2026-08-31.
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