Machine learning model development

Assign model work and the engineer builds a reproducible pipeline with baselines, leakage checks, metric-driven evaluation, error analysis, and a review-ready PR or report.

Join the waitlist

What it installs

Agents 1

  • ML Engineer

    Defines baselines, guards against leakage, evaluates models, writes error analysis, and drafts a PR or report for review.

Goals 1

  • Models evaluated before shipping

    Keep model changes reproducible, baseline-backed, and honestly evaluated.

Skills 1

  • scikit-learn

    Classical ML workflow guidance for scikit-learn pipelines, preprocessing, model selection, cross-validation, metrics, leakage prevention, and reproducible experiments. Adapted from jackspace/claudeskillz/scikit-learn.

Requirements

What this template expects to do its job. Task Machine does not verify these — you decide whether your setup is ready.

  • Connected repository or notebook workspace — Needs access to the modeling code, notebooks, experiment scripts, or pipeline repository where the change should land.
  • Dataset and target metric — Needs a dataset or sample, the prediction target, and the metric that decides whether the model is better.

Get started

Install Machine learning model development and run it with approvals.

Join the waitlist and we will send early access when the first private beta spots open.

Private beta. We invite teams in batches and never share your email.