Scikit Learn

This is your go-to for classical ML tasks in Python. Covers the full workflow: preprocessing and scaling, training classifiers and regressors (tree-based, linear, SVM, neural nets), clustering and dimensionality reduction, plus cross-validation and hyperparameter tuning. The pipeline examples are solid, especially the ColumnTransformer pattern for mixed data types. Tested against scikit-learn 1.8.0 with Python 3.11-3.14. One thing to note: this handles structured and text data well, but if you're doing deep learning or working with images beyond simple feature extraction, you'll want a different tool. Strong skill for interpretable, production-ready ML.

skills.sh installs: 2.3K
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last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill scikit-learn --agent claude-code

Installs into .claude/skills of the current project.

Files

Scikit-learn

Overview

This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.

Installation

Targets scikit-learn 1.9.1, verified with Python 3.13. The release requires Python 3.11+; use its published wheels for your interpreter/platform. See the 1.9 release notes. The bundled scripts and regression tests are executable examples. Reference snippets using caller-provided X, y, columns, or placeholders are illustrative adaptations, not complete standalone programs.

Install the PyPI package scikit-learn (not the deprecated sklearn package on PyPI). Import in code as sklearn.

# Install scikit-learn using uv
uv pip install "scikit-learn==1.9.1"

# Optional: plotting utilities and bundled script dependencies
uv pip install "scikit-learn[plots]==1.9.1" matplotlib pandas

# Commonly used with
uv pip install pandas numpy

SKILL.md on GitHub