gpt5.5 and most other models are very bad at one-shotting nice data models gpt5.5 also has this annoying property that once it decides for a schema (or any design), it's very hard to trigger thinking again. and if you ask to "rewrite from scratch", it will write create something even more ridiculous To solve this problem, I have built a meta-harness over codex just for simplifying slop data models called schemator (work in progress) Basic idea: it mimics what I myself do while I am designing a schema: scrutinize and question each field one by one It starts a fresh codex session for each field with a fixed prompt like "Try to come up with the most Lindy data model" + a prompt for side notes It does that with a fresh context for each field, so that they are independent from each other. At the end of a review run over a field, the reviewer can propose to keep, rename or remove the field When all fields are reviewed once, that makes one iteration. Then this is looped over until the review results stabilize, and do not propose any further changes I get better results by just asking my agent to "use schemator on this" after it creates a JSON schema or SQL table Give it a try if you have codex! It has a skill, so should be easy for an agent to figure out how to use