Standardize R preprocessing with mold() and forge() for modeling packages.
npx skills add https://github.com/jsperger/llm-r-skills --skill hardhat
Please help me install this Agent Skill. Skill: hardhat Source: https://github.com/jsperger/llm-r-skills/tree/main/skills/hardhat Command: npx skills add https://github.com/jsperger/llm-r-skills --skill hardhat
This Skill standardizes preprocessing workflows for R modeling packages, ensuring consistent handling of formula, XY, and recipe inputs during training and prediction.
Install and attach the hardhat package, then run a basic mold() workflow to preprocess training data and a subsequent forge() call for new data using a blueprint.
High-intent search queries and answers about installing and using this skill.
Standardize R preprocessing workflows by using mold() for training data and forge() for prediction data, ensuring consistent inputs and outputs across formula, XY, and recipe interfaces.
Build reproducible preprocessing pipelines in R by implementing interchangeable blueprints, model constructors, and validation hooks that enforce consistent data handling during training and deployment.
Yes, mold() and forge() support tidymodels by providing interchangeable blueprints for formula, XY, and recipe interfaces, ensuring consistent data handling across training and prediction.
Blueprint handlers in R work by applying specific preprocessing rules to training data with mold() and new data with forge(), ensuring consistent input and output shapes.
Yes, use spruce formatting alongside model constructors and validation hooks to ensure data integrity and consistent output formatting across different preprocessing interfaces.
Use validation hooks with mold() and forge() when you need to enforce data integrity and consistent inputs across end-to-end R modeling pipelines during training and prediction.
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