Arindol Sarkar focused on enhancing the robustness of ROC-AUC metric handling in the probabl-ai/skore repository by addressing issues with estimators lacking the predict_proba method. He implemented a defensive guard, _check_roc_auc, in Python to ensure ROC-AUC is only reported when supported, preventing runtime errors and reducing support overhead. Leveraging his skills in API design and software engineering, Arindol applied defensive programming patterns at API boundaries, which improved maintainability and reliability for mixed estimator types. This targeted bug fix deepened the stability of metric reporting, enabling more consistent model evaluation across diverse machine learning workflows within the project.