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 skills add richfrem/agent-plugins-skills --skill rlm-auditgit 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/skills/richfrem/agent-plugins-skills/rlm-audit)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/rlm-audit"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/rlm-audit.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00259 |
| Opus 5 | $0.00008 | $0.00130 |
| Sonnet 5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
rlm-audit 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 4d 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
rlm-audit
Purpose
Systematically audit the RLM (Recursive Language Model) semantic cache to identify gaps between the project manifest and the actual summary files stored on disk.
Capabilities
- Coverage Analysis: Calculates the percentage of project documentation currently summarized.
- Gap Identification: Detects files that are included in the manifest but missing from the cache.
- Exporting: Generates CSV lists of missing files for batch processing.
- Validation: Ensures the cache directory structure mirrors the source repository.
Usage
Run the audit for a specific profile to see what's missing:
python scripts/audit_cache.py --profile wiki --report audit_report.txt --csv missing_files.csv
Related
- rlm-distill-agent: Use to fill the gaps identified by this audit.
- rlm-cleanup-agent: Use to remove stale entries from the cache.
What ships with it
4 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.
- 4d ago First seen · 33 lines · 15 tokens per session scan A 37eeb19d14a3
rlm-audit is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 259 once invoked, about $0.0001 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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