audit-ml-pipeline

A collection of ML experimentation skills for Python built around skrub, scikit-learn, and skore, covering the full PyData ecosystem pipeline from data sourcing to model evaluation.

A skill for ML experimentation in Python organized around the PyData ecosystem, providing guidance on skrub, scikit-learn, and skore for data processing, model training, and experiment tracking.

A collection of 55+ ML experimentation skills for Python organized around skrub, scikit-learn, and skore within the PyData ecosystem. Skills cross-reference each other for iteration loops, sourcing strategies, test routing, and symbol lookups.

A collection of ML experimentation skills for Python organized around skrub, scikit-learn, and skore. Covers the full ML pipeline lifecycle including data sourcing, feature engineering, model evaluation, and iteration loops for 55+ AI coding agents.

A collection of 55+ ML experimentation skills for Python, organized around skrub, scikit-learn, and skore within the PyData ecosystem. Supports multiple coding agents including Claude Code, Codex, and Cursor, with cross-referencing between skills for iteration loops, sourcing strategies, and smoke tests.

Part of the Probabl AI skills collection for ML experimentation in Python, organized around skrub, scikit-learn, and skore within the PyData ecosystem, with support for 55+ coding agents including Claude Code and Codex.

A skill bundle for ML experimentation in Python, built around skrub, scikit-learn, and skore, covering pipeline construction, evaluation, testing, and iterative experiment workflows in the PyData ecosystem.

Part of a collection of ML experimentation skills for Python built around skrub, scikit-learn, and skore, supporting the full ML pipeline lifecycle within the PyData ecosystem.

A collection of ML experimentation skills for Python built around skrub, scikit-learn, and skore, covering the PyData ecosystem with cross-referencing workflows for pipeline building and testing.

Part of Probabl's ML experimentation skills organized around skrub, scikit-learn, and skore, this skill provides smoke testing patterns for machine learning pipelines in the PyData ecosystem with support for 55+ coding agents.