Skore is an open source machine learning library that helps data scientists evaluate models and apply recommended practices throughout the ML development lifecycle. This talk dives into one of its core features: "checks," automated diagnostics that run on any scikit-learn-compatible estimator and flag issues with stable, actionable error codes. We'll see how checks catch common modeling pitfalls out of the box, how teams can write custom checks to encode their own business and compliance requirements, and how their structured output makes them a natural fit for agentic ML development loops.