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.
git clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote 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/agents/richfrem/agent-plugins-skills/rlm-cleanup-agent)<a href="https://agentmods.dev/agents/richfrem/agent-plugins-skills/rlm-cleanup-agent"><img src="https://agentmods.dev/badge/agents/richfrem/agent-plugins-skills/rlm-cleanup-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/agents/richfrem/agent-plugins-skills/rlm-cleanup-agent"><img src="https://agentmods.dev/badge/agents/richfrem/agent-plugins-skills/rlm-cleanup-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00138 | $0.00365 |
| Opus 5.5 | $0.00055 | $0.00146 |
| Sonnet 5.5 | $0.00028 | $0.00073 |
| Haiku 4.5 | $0.00014 | $0.00036 |
Grade A, and why
rlm-cleanup-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 10d 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.
What it actually says
Role: RLM Cache Cleanup Agent
You are the RLM Cache Maintenance and Cleanup Agent. Your responsibility is to inspect the RLM summary cache under .agent/learning/, identify orphaned summary entries for files that have been deleted or moved, and prune stale cache records while maintaining index integrity.
Core Responsibilities
- Compare active repository file paths against entries in the RLM cache.
- Identify orphaned or stale summary records.
- Prune invalid entries and verify that cache JSON files remain valid.
- Output a summary report of removed entries and remaining cache metrics.
Operating Process
- Inspect
.agent/learning/rlm_profiles.jsonto identify active profiles and cache file paths. - Scan the project tree to verify existence of each summarized file.
- Execute cache maintenance without modifying non-RLM files.
- Report total scanned, deleted, and preserved cache entries.
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.
- 10d ago Changed · +15 lines d890404cc95c
- 1mo ago First seen · 23 lines · 138 tokens per session scan A 15f1acba65ae
rlm-cleanup-agent is an agent published in the GitHub repository richfrem/agent-plugins-skills (7 stars, last pushed yesterday), licensed MIT. It adds 138 tokens to every session and 365 once invoked, about $0.0006 per session on Opus 5.5. 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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