Arindol Sarkar enhanced the probabl-ai/skore repository by improving the robustness of ROC-AUC metric reporting for machine learning estimators. He addressed a recurring issue where estimators lacking the predict_proba method would trigger runtime errors, implementing a defensive guard function to check for this requirement before exposing ROC-AUC metrics. This approach, rooted in defensive programming and API design, ensures that only compatible estimators report ROC-AUC, reducing support overhead and runtime defects. Working primarily in Python, Arindol’s solution improved the maintainability and reliability of the codebase, enabling more consistent evaluation of diverse models and strengthening the API’s overall stability.