ml-data-audit

Check whether a dataset is fit for machine learning, and catch the problems that silently ruin models. Always use this skill when someone asks which columns leak the target, whether a feature will actually be available at prediction time, why accuracy looks suspiciously high, or whether training and serving data differ, even when the question sounds like a quick check and even before the data is shared. Also use it when someone shares or describes a dataset for modelling and asks whether it is usable, what is wrong with it or what to clean; or mentions missing values, duplicates, sentinel codes like 999 or -1, outliers, skewed or mixed-scale columns, class imbalance, label quality, how the target was defined, label delay, or post-outcome columns. Not for designing new features, and not for choosing or tuning a model.