tidymodels

tidymodels/skills

Plugin Claude Code

Plugin marketplace listing 2 plugins: tidymodels-developers, tidymodels-users.

tidymodels/skills

Plugin Claude Code

Claude Code skills for the Tidymodels ecosystem — developer tools for building extensions and user guides for data analysis and modeling.

tidymodels/skills

Hook Claude Code

Runs before the agent uses a tool for Bash tool calls, running an inline shell check. From tidymodels/skills.

tidymodels/skills

Settings file Claude Code

Agent settings declaring 1 hook event (PreToolUse).

tidymodels/skills

Skill Claude CodeCodex

Guide for creating new dials parameters for hyperparameter tuning. Use when a developer needs to define custom tuning parameters for models, recipes, or workflows, including quantitative parameters (continuous/integer), qualitative parameters (categorical), parameters with transformations, and data-dependent…

tidymodels/skills

Skill Claude CodeCodex

Add new computational engines to existing parsnip models. Use when connecting an existing parsnip model (linearreg, boosttree, etc.) to a new computational backend or R package.

tidymodels/skills

Skill Claude CodeCodex

Create entirely new model specifications for the parsnip package. Use when creating a fundamentally new model type (like linearreg, boosttree) with its constructors, registration, and engine implementations. For adding engines to existing models, use add-parsnip-engine instead.

tidymodels/skills

Skill Claude CodeCodex

Create a new preprocessing step for the recipes package following tidymodels conventions.

tidymodels/skills

Skill Claude CodeCodex

Guide for creating new yardstick metrics. Use when a developer needs to extend yardstick with a custom performance metric, including numeric, class, probability, ordered probability, survival (static, dynamic, integrated, linear predictor), and quantile metrics.

tidymodels/skills

Skill Claude CodeCodex

Build machine learning models using tidymodels for tabular data using proper data spending, resampling, and validation practices. Covers train/test splitting, cross-validation, feature engineering, model tuning, and evaluation. Use when building predictive models, comparing algorithms, or when users mention machine…