What are spruce functions used for?

Spruce functions like spruce_numeric() and spruce_class() are used to format raw model outputs into standardized, user-friendly tibbles.

How does mold() differ from forge()?

mold() is used during the training phase to process the initial data and create a blueprint, while forge() uses that blueprint to ensure new prediction data is processed identically.

Can this skill help with factor level issues in R?

Yes, it implements the forge() validation logic which automatically checks for missing columns, incompatible types, and novel factor levels in new data.

What is the primary purpose of the hardhat skill?

The hardhat skill helps developers build R modeling packages that follow consistent API patterns, specifically for data preprocessing and prediction validation.

Does this skill support the tidymodels ecosystem?

Yes, hardhat is a core part of the tidymodels infrastructure, and this skill follows its design principles for building interoperable modeling tools.

Hardhat R Modeling Infrastructure

Name: Hardhat R Modeling Infrastructure
Author: jsperger

byjsperger

•

Data Science & ML

Standardizes the creation of R modeling packages by providing consistent preprocessing interfaces and output formatting patterns.

This skill equips Claude with the specialized knowledge to develop robust R modeling packages using the hardhat framework, ensuring a consistent user experience across formula, data frame, matrix, and recipe interfaces. It guides the implementation of core functions like mold() for training data preprocessing and forge() for prediction data validation, while enforcing tidymodels-compliant output standards through spruce functions. By following these established patterns, developers can build high-quality, interoperable modeling tools that handle complex data transformations and validation logic automatically.

Key Features

01Standardized preprocessing with mold() and forge() functions

021 GitHub stars

03Consistent output formatting using spruce functions for predictions

04Uniform support for formula, matrix, data frame, and recipe interfaces

05Structured boilerplate for model constructors and bridge functions

06Automated prediction data validation and factor level consistency checks

Use Cases

01Building a new R package for a custom machine learning algorithm

02Implementing standardized prediction interfaces for complex statistical models

03Updating an existing R model to support tidymodels-style blueprints

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jsperger/llm-r-skills hardhat

For use in Claude.ai and ChatGPT

Download Skill