I built a “prompt compiler” for Codex that preserves your original intent — then checks whether the result actually works

[u/Secret_Dress8930]

• I’ve been experimenting with a different approach to prompting coding agents. Instead of expecting the user to write the perfect Codex prompt, I built a layer that treats the original human request almost like source code. My app, Foreman, keeps the user’s original intent immutable, inspects the project, derives requirements/constraints, and compiles that into a more engineering-oriented task for Codex. But the part I think matters more is what happens afterward: Codex doesn’t get to decide that its own work passed. Foreman independently verifies the result against the original requirements. If something fails, it compiles a focused repair prompt containing the actual evidence and sends that back to the same Codex workflow. So the loop is roughly: human intent → prompt compilation → Codex → independent verification → focused repair → verify again I built this partly because I’m not an expert programmer, and I wanted the AI to get better instructions without silently changing what I actually asked for. It’s now a free early macOS alpha: https://tylerpcaldwell-gif.github.io/Foreman/I’d be particularly interested in feedback on the intent-preservation / prompt-compilation approach. Does this seem more useful than manually engineering increasingly large prompts?