I built a baseline-aware Python code health tool for CI and AI-assisted coding

I built a baseline-aware Python code health tool for CI and AI-assisted coding If you write Python with AI tools today, you’ve probably felt this already: the code usually works, tests may pass, lint is green, but the structure gets worse in ways that are hard to notice until the repository starts fighting back. Not in one dramatic commit. More like this: the same logic gets rewritten in slightly different ways across multiple files; helper functions quietly grow until nobody wants to touch them; coupling increases one import at a time; framework callbacks look unused even when they are not; dead code accumulates because generated code tends to leave leftovers behind. That is the problem space I built CodeClone for. CodeClone 2.0.0b1 is the first version where the tool really matches the model I wanted from the beginning: not just “find some clones,” but track structural code health over time , in CI, with a trusted baseline. This post is an introduction to that version and the design