Does this skill handle R code styling or linting?

No, this skill focuses specifically on function architecture and API design. It does not cover linting, syntax styling, or error handling.

Does it provide guidance on S3 objects?

Yes, it includes standardized patterns for S3 constructors, validators, and user-facing helpers to ensure consistent object-oriented behavior.

How does this skill improve R function discoverability?

It encourages the use of common prefixes (like str_ or read_) which allows IDEs to group related functions in autocomplete menus.

Is this skill only applicable to tidyverse packages?

While it uses Tidy Design Principles, these patterns are best practices for any R project where readability and composability are priorities.

Tidy R Function Design

Name: Tidy R Function Design
Author: jsperger

byjsperger

•

Data Science & ML

Designs and reviews R function APIs to ensure they are predictable, pipe-friendly, and follow Tidy Design Principles.

About

The Tidy R Function Design skill provides specialized guidance for creating R functions optimized for humans rather than just computers. It focuses on reducing cognitive load through predictable naming conventions, logical argument ordering, and output type stability. By applying these principles, Claude can help you build R packages and scripts that are discoverable via autocomplete, easy to compose with the pipe operator, and robust against common design pitfalls like global option dependencies or complex boolean flags. This skill is essential for R developers looking to mirror the consistency and usability of the tidyverse in their own projects.

Key Features

Standardizes naming using imperative verbs and autocomplete-friendly prefixes.
Implements strategy patterns using objects to replace confusing boolean flags.
1 GitHub stars
Optimizes function signatures for pipe-friendliness by placing data arguments first.
Ensures type stability so output types are predictable from input types.
Provides S3 constructor and validator patterns for clean model object design.

Use Cases

Refactoring legacy R code to improve consistency and readability.
Designing a user-facing API for a new R package.
Reviewing R function signatures for argument ordering and naming best practices.

About

Key Features

Standardizes naming using imperative verbs and autocomplete-friendly prefixes.
Implements strategy patterns using objects to replace confusing boolean flags.
1 GitHub stars
Optimizes function signatures for pipe-friendliness by placing data arguments first.
Ensures type stability so output types are predictable from input types.
Provides S3 constructor and validator patterns for clean model object design.

Use Cases

Refactoring legacy R code to improve consistency and readability.
Designing a user-facing API for a new R package.
Reviewing R function signatures for argument ordering and naming best practices.