# Matlab Engineer Tabular Features

Use when engineering or selecting the best features for single-response classification...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: Data and ML
- License: see repository
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-engineer-tabular-features -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-engineer-tabular-features -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-engineer-tabular-features -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-engineer-tabular-features -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

Use when engineering or selecting the best features for single-response classification or regression in MATLAB, whatever the data's modality, for non-tabular data it routes extraction to a domain skill, then selects, assesses, and delivers on the resulting table. Not for multi-response problems, model training, or raw data acquisition.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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](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](references/domain-routing.md)).

## When NOT to Use

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/ai-and-statistics/matlab-engineer-tabular-features/SKILL.md