vector-db-cleanup — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited vector-db-cleanup (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.
This skill requires the chromadb and langchain packages defined in the plugin root.
You remove stale and orphaned chunks from the ChromaDB vector store. A chunk is stale when its source file no longer exists on disk. Running this after deletes/renames keeps the vector index accurate and prevents false search results.
This is a write (delete) operation.
query.py returns results pointing to non-existent files.This skill defaults to In-Process mode for zero-latency direct disk access. No background server is required.
Verify available profiles in .agent/learning/vector_profiles.json. The default profile is usually wiki.
Note: The --profile flag is mandatory to ensure the correct collection and disk paths are loaded.
python ./scripts/cleanup.py --profile wikiRun the consistency check to verify that remaining facts are still supported.
python ./scripts/vector_consistency_check.py --profile wiki --topic .agent/learning/--profile to ensure the correct semantic space is pruned.cleanup.py.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.