I open-sourced a Codex skill that turns vague prompts into intent-preserving execution loops

A lot of agent workflows get more capable as they get less faithful. I kept running into the same problem: you give an agent a messy real-world prompt, and it either drifts from your intent, expands scope on its own, or produces something hard to verify. So I made a Codex skill called prompt-to-loop-engineer. What it does: - locks the original intent first - turns vague prompts into a looped execution contract - adds anti-drift checks - handles coding, analysis, planning, and creative tasks differently - makes outputs easier to validate and iterate on I’m trying to make agents more usable for real work, not just more verbose. Would love feedback on: - whether this solves a real pain point - where the loop design is still weak - what kinds of tasks break this approach