The 20% flagged rate is striking and honestly matches what I expected. The skill ecosystem grew fast and the trust model was essentially trust-the-repo, trust-the-author — fine when you read the code, but nobody actually does that at scale.

A few things I would want to know as a production user:

1. False positive rate. If I am blocking 20% of skills and half are legitimate, I will disable the scanner. What is the precision on the THREAT tier vs. CAUTION?

2. What counts as a threat pattern? Reverse shells and credential theft are obvious. But "prompt injection buried in configs" is more interesting — is this heuristic-based (pattern matching) or semantic (understanding what the injection is trying to do)?

3. Integration path. The ideal UX is not paste-a-URL-before-installing — it is a CLI wrapper that scans first then installs if clean. Or a pre-install hook OpenClaw could call natively. Any plans there?

The crowdsourced angle is smart. Security knowledge about what is actually dangerous should compound well over 6,500+ scans.