I built a Codex plugin that makes AI agents prove “done” — the evidence loop that worked

I’m building Superloopy, a small MIT-licensed Codex plugin/CLI for AI coding-agent workflows.

The build started from a pretty annoying failure mode: an agent says “done,” but I can’t quickly tell what changed, what actually ran, whether the original acceptance criteria survived, or what evidence backs the final answer.

The loop that has worked best so far is intentionally boring:

1. write acceptance criteria before the agent starts
2. run real commands/checks against each criterion
3. save receipts under .superloopy/evidence/
4. block the final “done” report until the claims line up with the evidence

The hard part was keeping this from becoming too much ceremony. I don’t want a giant PRD process for every tiny task, so the default path is meant to stay lightweight:

loopy <task>

For larger tasks, Superloopy also ships specialist skills. The strongest stress test so far is superloopy-clone, for authorized website rebuilds: it captures screenshots, DOM/topology, computed styles, assets, behavior notes, component specs, build output, and visual QA before claiming success. That forced the evidence trail to cover visual/behavioral claims, not just unit-test output.

Main build lesson: “AI agent trust” feels less like asking the model to be honest and more like designing the workflow so the final answer has receipts attached.

Repo: https://github.com/beefiker/superloopy

I’d love feedback from people building with coding agents:

• does this “proof of done” loop feel useful or too heavy?
• what receipts would make you trust an agent’s final answer?
• should this stay Codex-specific, or become a generic harness for different coding agents?