Skill UI

Generates Python code and workflows for collaborative, content-based, or hybrid recommendation models, handling data prep, training, evaluation, and scalability so Claude can deliver tailored suggestions across domains.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

Guides developers through scikit-learn workflows for classical supervised and unsupervised tasks, data preprocessing, evaluation schemes, hyperparameter tuning, and end-to-end pipelines when building interpretable production models.

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

This skill provides comprehensive command-line interface (CLI) functionality for managing the entire lifecycle of experiments within the Arize platform. Users can list, retrieve metadata, create new experiments from various data files (CSV, JSON, Parquet), and export detailed run results for deep analysis. It supports linking experiments to specific dataset versions, making it essential for structured ML model evaluation and performance comparison workflows.

This skill provides comprehensive command-line capabilities for interacting with Arize datasets. Use it to perform dataset CRUD operations (Create, Read, Update), list available datasets, retrieve detailed metadata, and export large volumes of example data. It is essential for managing the lifecycle of versioned datasets used in model evaluation and machine learning experimentation. Supports file formats like CSV, JSON, and Parquet.