giacomogaglione

Profile and explore datasets -- schema inference, distributions, missing values, outliers, correlations. Use when starting work with a new dataset or investigating data quality issues.

Debug ML issues systematically -- tensor shapes, NaN propagation, gradient flow, device mismatches, dtype errors. Use when encountering training failures, unexpected outputs, or numerical issues.

Extract Jupyter notebook cells into tested Python modules while preserving the exploration workflow. Use when converting prototyping notebooks into production code.