r

R language expert skill. Use when writing, reviewing, or debugging R code, or when the user asks for R best practices, idiomatic R, or guidance on the R ecosystem. Covers base R, tidyverse style, vectorization, pipe usage, error handling, and performance patterns. Complements r-lib (package dev) and shiny (web apps) — this skill focuses on the language itself.

Build command-line apps in R using the Rapp package. Use when creating a CLI tool in R, adding argument parsing to an R script, turning an R script into a command-line app, shipping CLIs in an R package, or using Rapp (the alternative Rscript front-end). Also use for shebang scripts, exec/ directory in R packages, or subcommand-based R tools.

Comprehensive R package for command-line interface styling, semantic messaging, and user communication. Use this skill when working with R code that needs to: (1) Format console output with inline markup and colors, (2) Display errors, warnings, or messages with cli_abort/cli_warn/cli_inform, (3) Show progress indicators for long-running operations, (4) Create semantic CLI elements (headers, lists, alerts, code blocks), (5) Apply themes and customize output styling, (6) Handle pluralization in user-facing text, (7) Work with ANSI strings, hyperlinks, or custom containers. Also use when migrating from base R message/warning/stop, debugging cli code, or improving existing cli usage.

Prepare R packages for CRAN submission by checking for common ad-hoc requirements not caught by devtools::check(). Use when: (1) Preparing a package for first CRAN release, (2) Preparing a package update for CRAN resubmission, (3) Reviewing a package to ensure CRAN compliance, (4) Responding to CRAN reviewer feedback. Covers documentation requirements, DESCRIPTION field standards, URL validation, examples, and administrative requirements.

Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.