dataset-profiling

Use as the FIRST step of any ML task, before choosing a model, to inspect and understand the actual dataset. Works for a LOCAL dataset (Claude reads the files directly) and for a KAGGLE dataset (Claude cannot read /kaggle/input from your machine, so it emits a small profiling cell you run on Kaggle and paste back, or downloads via the Kaggle API). Profiles structure, size, modality, schema, class balance, the entity for group splitting, and any official split. Triggers on 'my dataset', 'data is on kaggle', 'CSV', 'data folder', 'profile the data', 'what does the dataset look like', 'EDA', 'explore the data'.