probe-eval — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited probe-eval (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
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.
Score residual-stream activations with a published probe direction. Probes are loaded into the Colab backend via the launch script or via separate probe-loading utilities; this skill only evaluates.
After a capture-acts call, when the researcher wants to know "does this probe fire on these activations" or "what's the AUROC of probe X on these labels".
Call the MCP tool `probe_eval` with:
probe_id: ID of a probe already loaded (run list_probes first if unsure)capture_id: from a prior capture-acts calllabels (optional): list of 0/1 labels for the captured rows. If provided, return AUROC.layer (optional): override the probe's declared layerIf the researcher reports AUROC > 0.9 with N < 50 and asks "is this a real result?":
causality-protocol to apply the three mandatory checks (random-feature baseline, control-token normalization, structural-rigidity α-sweep).causality-protocol separates signal from over-parameterization.Compact:
"Probe {probe_id}@L{layer}: AUROC {auroc:.3f} on {n_samples} samples. Mean score {mean:.3f}. Manifest {sha[:12]}."
Avoid claims like "this is causal" or "this is the real direction" — probe_eval is detection-only; causality needs the protocol.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.