Skill audit report

build-ml-pipeline Audit report.

Declare the pipeline from data source to predictor as a **skrub DataOps graph** (not as a bare `sklearn.Pipeline`). Every step is either a pure-Python function (stateless) attached via `.skb.apply_func`, or a sklearn-compatible estimator (stateful) attached via `.skb.apply`. Stops at the declared object — no fit, split, tuning, persistence, or evaluation. TRIGGER — any of: - Writing or editing code that declares any link in the chain *data source → predictor*: loaders, preprocessing, encoders / imputers / scalers, feature steps, composition objects (`Pipeline`, `ColumnTransformer`, skrub `tabular_pipeline`, `nn.Module`), or the final estimator. - A pure-Python data-processing function destined for the pipeline path (cleans / derives / reshapes) — whether wrapped via `FunctionTransformer`, `skrub.@deferred` / `skrub.var`, a custom `BaseEstimator` subclass, or just called in the training path before the estimator. - A step is added, removed, swapped, or reordered inside an existing pipel