Pierre-Yves Chabaud enhanced the Subaru-PFS/datamodel repository by strengthening error handling within the pfsZCandidates data model. He introduced a new NO_ERROR value to the ZLError enumeration, addressing a missing error code and improving the model’s reliability and diagnosability for downstream data workflows. Using Python, he focused on robust data modeling and precise error handling, ensuring that the changes maintained backward compatibility and long-term maintainability. The targeted update was validated through careful review, reflecting a thoughtful approach to evolving the codebase. Over the month, Pierre-Yves delivered a focused feature that deepened the project’s error management capabilities without introducing regressions.