Can I use this for CRAN package development?

Absolutely. It enforces namespace qualification and specific roxygen2 documentation patterns that are essential for passing CRAN's rigorous R CMD check process.

Which pipe operator does this skill recommend?

The skill prefers the native R pipe (|> ) introduced in R 4.1 over the magrittr pipe (%>%), promoting modern R syntax while ensuring readability in long chains.

How does it improve documentation?

It identifies and replaces circular or generic documentation (e.g., '@param data The data') with specific, descriptive text that details the structure and expectations of inputs and outputs.

What is 'slop' in the context of R programming?

Slop refers to generic, unoptimized code patterns often produced by AI, such as using vague variable names (df, result), omitting namespace prefixes (::), and relying on implicit returns which make debugging difficult.

Does this skill work with Quarto and RMarkdown?

Yes, the R Anti-Slop skill specifically applies to .R, .Rmd, and .qmd files, ensuring your data stories and reports maintain the same quality as your library code.

R Anti-Slop: Clean Code Standards

Name: R Anti-Slop: Clean Code Standards
Author: emraher

byemraher

데이터 과학 및 ML

Enforces production-quality R coding standards by eliminating generic AI patterns and implementing rigorous namespace and tidyverse conventions.

소개

R Anti-Slop is a specialized skill designed to elevate R programming from generic, AI-generated 'slop' to production-ready code suitable for packages and CRAN submissions. It enforces high-level engineering standards including mandatory namespace qualification (pkg::fun), explicit return statements, and descriptive snake_case naming conventions. By preventing common shortcuts like naming variables 'df' or 'result' and ensuring realistic roxygen2 documentation, this skill ensures that data analysis scripts, Quarto documents, and R packages remain maintainable, readable, and statistically rigorous.

주요 기능

Support for native R pipe (|>) conventions and Tidyverse best practices
Validation of descriptive snake_case naming, banning generic names like df and result
Automated enforcement of namespace qualification for external package functions
Integration with styler and lintr for automated code formatting and linting
Transitioning from implicit returns to explicit return() statements for clarity
0 GitHub stars

사용 사례

Preparing R packages for CRAN submission by ensuring strict adherence to documentation and code quality standards
Refactoring AI-generated R scripts into production-grade data analysis pipelines
Standardizing team-wide code quality in collaborative Quarto and RMarkdown research projects

소개

주요 기능

Support for native R pipe (|>) conventions and Tidyverse best practices
Validation of descriptive snake_case naming, banning generic names like df and result
Automated enforcement of namespace qualification for external package functions
Integration with styler and lintr for automated code formatting and linting
Transitioning from implicit returns to explicit return() statements for clarity
0 GitHub stars

사용 사례

Preparing R packages for CRAN submission by ensuring strict adherence to documentation and code quality standards
Refactoring AI-generated R scripts into production-grade data analysis pipelines
Standardizing team-wide code quality in collaborative Quarto and RMarkdown research projects