How to get your AI skill certified, display your badge, and maintain certification over time.
Write a JSON manifest describing inputs, outputs, dependencies, and failure modes.
Three-pillar scoring: functional (40%), security (35%), performance (25%). Fully automated.
Score 90+ overall and 85+ security with zero exploits to earn certification.
Review your report, fix the issues, submit again. Most skills fail on first attempt.
Your manifest tells the evaluator what your skill does. Richer manifests score higher in functional evaluation.
What your skill accepts. Be specific.
What your skill returns. Name each output field. The evaluator validates these against actual responses.
External packages. These get audited for known CVEs during security evaluation.
How your skill can fail. Declaring failure modes shows maturity and helps test error handling.
Plain English description. Used to generate functional test scenarios.
Network, file system, or other permissions needed. Narrower scopes score better in security.
How you handle user data. "No data stored" is the gold standard.
Once certified, show it. Three options with copy-paste code.
Here is what the badge looks like when embedded:
The badge updates automatically. If certification is suspended, the badge reflects that immediately.
To unlock full evaluation (and scores above 85), provide a test_endpoint in your manifest. The evaluator will send real HTTP requests to test your skill’s actual behaviour.
Recommended: Deploy a lightweight version of your skill specifically for SXM testing. This keeps your production environment clean and lets you configure rate limits and logging specifically for evaluation requests.
test_timeout_ms.Certification is not a one-off event. It is an ongoing relationship. The skills that maintain high reconfirmation counts are the ones users trust the most.