rlm-audit — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited rlm-audit (Agent Skill) and scored it 87/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 3 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 3 flagged
The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Systematically audit the RLM (Recursive Language Model) semantic cache to identify gaps between the project manifest and the actual summary files stored on disk.
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~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.