Engineer Tabular Features

A lean, functional pipeline: intake → feature pool → select → assess → deliver → report. There is no shared context object, each phase is a direct call to leaf utilities in scripts/, and the reference file for each phase carries the detail. Your value is the structured, data-driven process and, above all, the consensus selection at its center, not an ad-hoc answer.

This skill bundles the workflow in references/ (per-phase detail read on demand) and scripts/ (the computation and plotting utilities). Do not invoke files in references/ as separate skills, they are loaded only via the Read tool when the phase that needs them runs. The per-phase files call the pipeline's leaf helpers for you; if you ever need a helper's signature, references/internal-helpers.md documents each one's inputs, outputs, and an example call, so you never open a helper's source.

When to Use

• Engineering or selecting the best predictors for single-response supervised classification or regression on a plain in-memory table.
• You want a structured, data-driven selection, a ranker panel, a consensus vote, and an elbow cut, rather than an ad-hoc hand-picked feature set.
• The data is non-tabular (signals, images, battery/machinery telemetry): this skill routes extraction to the matching domain skill, then engineers, selects, assesses, and delivers on the resulting table (see references/domain-routing.md).

When NOT to Use