Agent skill registries screen what they publish. OpenClaw's security team reported that its scanners overlap on at most 10.4% of combined positives, and 81.9% of flagged skills are caught by one scanner alone. We take that as given, and suggest the open question is not which scanner is right but which is being asked. Each answers a form of "is this skill malicious?", which is what it was built for. "Is this action permitted here, by this operator, right now?" is not one it is designed to express. We report three measurements over 66,192 public ClawHub skill versions. First, 705 skills across 135 distinct publishers that every scanner and the registry's judge rate clean nonetheless instruct an action prohibited by CIS Control 2.7 and NIST SP 800-53 CM-11. A hand audit of 100 puts our detector at 92% precision and found no marker of malicious intent. One publisher contributes 506 of the 705, so we report the distribution with the count. This is reproducible from public artifacts. Second, of 144 commands a live agent executed in a sandbox while following real skill documentation, 34.7% carried a consequence class absent from that document. Third, over 53 cleared skills documenting an action no clean record earns, the agent reached for one in 23 and the gate stopped all 23. The harness and every recorded command are released. We offer one design for that gap: a deterministic resolver with no model in the decision path, feeding a per-(resource, class) trust ledger whose promotion thresholds derive from the operator's stated risk tolerance. Ten clean approvals cannot exclude a true failure rate of 25.9% at 95% confidence. We price the gate's interruptions across a spectrum of operator policies rather than quote one false-positive rate, since friction is a property of the policy, not the gate. We release a 64-case obfuscation benchmark; ours resolves 52%.

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