1. You layout policy stating that all code, especially AI code has to be written to a high quality level and have been reviewed for issues prior to submission.
2. Given that even the fastest AI models do a great job of code reviews, you setup an agent using Codex-Spark or Sonnnet, etc to scan submissions for a few different dimensions (maintainability, security, etc).
3. If a submission comes through that fails review, that's a strong indication that the submitter hasn't put even the lowest effort into reviewing their own code. Especially since most AI models will flag similar issues. Knock their trust score down and supply feedback.
3a. If the submitter never acts on the feedback - close the submission and knock the trust score down even more.
3b. If the submitter acts on the feedback - boost trust score slightly. We now have a self-reinforcing loop that pushes thoughtful submitters to screen their own code. (Or ai models to iterate and improve their own code)
4. Submission passes and trust score of submitter meets some minimal threshold. Queued for human review pending prioritization.
I haven't put much thought into this but it seems like you could design a system such that "clout chasing" or "bot submissions" would be forced to either deliver something useful or give up _and_ lose enough trust score that you can safely shadowban them.
