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.
npx -y skills add k-dense-ai/scientific-agent-skills --skill scikit-learn --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
supercent-io/skills-template
supercent-io/skills-template
huangjia2019/claude-code-engineering
reactjs/react.dev
reactjs/react.dev