r-lib/cli/SKILL.md
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.
npx skillsauth add posit-dev/skills cliInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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task: Display error with context and formatting
use: cli_abort() with inline markup and bullet lists
task: Show warning with formatting
use: cli_warn() with inline markup
task: Display informative message
use: cli_inform() with inline markup
task: Show progress for counted operations
use: cli_progress_bar() with total count
task: Show simple progress steps
use: cli_progress_step() with status messages
task: Format code or function names
use: {.code ...} or {.fn package::function}
task: Format file paths
use: {.file path/to/file}
task: Format package names
use: {.pkg packagename}
task: Format variable names
use: {.var variable_name}
task: Format values
use: {.val value}
task: Handle singular/plural text
use: {?s} or {?y/ies} with pluralization
task: Create headers
use: cli_h1(), cli_h2(), cli_h3()
task: Create alerts
use: cli_alert_success(), cli_alert_danger(), cli_alert_warning(), cli_alert_info()
task: Create lists
use: cli_ul(), cli_ol(), cli_dl() with cli_li()
Use inline markup with {.class content} syntax to format text:
# Basic formatting
cli_text("Function {.fn mean} calculates averages")
cli_text("Install package {.pkg dplyr}")
cli_text("See file {.file ~/.Rprofile}")
cli_text("{.var x} must be numeric, not {.obj_type_of {x}}")
cli_text("Got value {.val {x}}")
# Code formatting
cli_text("Use {.code sum(x, na.rm = TRUE)}")
# Paths and arguments
cli_text("Reading from {.path /data/file.csv}")
cli_text("Set {.arg na.rm} to TRUE")
# Types and classes
cli_text("Object is {.cls data.frame}")
# Emphasis
cli_text("This is {.emph important}")
cli_text("This is {.strong critical}")
# Fields
cli_text("The {.field name} field is required")
Vectors are automatically collapsed with commas and "and":
pkgs <- c("dplyr", "tidyr", "ggplot2")
cli_text("Installing packages: {.pkg {pkgs}}")
#> Installing packages: dplyr, tidyr, and ggplot2
files <- c("data.csv", "script.R")
cli_text("Found {length(files)} file{?s}: {.file {files}}")
#> Found 2 files: data.csv and script.R
Use double braces {{ and }} to escape literal braces:
cli_text("Use {{variable}} syntax in glue")
#> Use {variable} syntax in glue
For complete markup reference: See references/inline-markup.md for all 50+ inline classes, edge cases, nesting rules, and advanced patterns.
Use {?} for pluralization with three patterns:
nfile <- 1
cli_text("Found {nfile} file{?s}")
#> Found 1 file
nfile <- 3
cli_text("Found {nfile} file{?s}")
#> Found 3 files
ndir <- 1
cli_text("Found {ndir} director{?y/ies}")
#> Found 1 directory
ndir <- 5
cli_text("Found {ndir} director{?y/ies}")
#> Found 5 directories
nfile <- 0
cli_text("Found {nfile} file{?s}: {?no/the/the} file{?s}")
#> Found 0 files: no files
nfile <- 1
cli_text("Found {nfile} file{?s}: {?no/the/the} file{?s}")
#> Found 1 file: the file
nfile <- 3
cli_text("Found {nfile} file{?s}: {?no/the/the} file{?s}")
#> Found 3 files: the files
Use no() to display "no" instead of zero:
nfile <- 0
cli_text("Found {no(nfile)} file{?s}")
#> Found no files
Use qty() to set quantity explicitly:
nupd <- 3
ntotal <- 10
cli_text("{nupd}/{ntotal} {qty(nupd)} file{?s} {?needs/need} updates")
#> 3/10 files need updates
For advanced pluralization: See references/inline-markup.md for edge cases and complex patterns.
Use cli conditions instead of base R for better formatting:
# Before (base R)
stop("File not found: ", path)
# After (cli)
cli_abort("File {.file {path}} not found")
# With bullets for context
check_file <- function(path) {
if (!file.exists(path)) {
cli_abort(c(
"File not found",
"x" = "Cannot read {.file {path}}",
"i" = "Check that the file exists"
))
}
}
# Before (base R)
warning("Column ", col, " has missing values")
# After (cli)
cli_warn("Column {.field {col}} has missing values")
# With context
cli_warn(c(
"Data quality issues detected",
"!" = "Column {.field {col}} has {n_missing} missing value{?s}",
"i" = "Consider using {.fn tidyr::drop_na}"
))
# Before (base R)
message("Processing ", n, " files")
# After (cli)
cli_inform("Processing {n} file{?s}")
# With structure
cli_inform(c(
"v" = "Successfully loaded {.pkg dplyr}",
"i" = "Version {packageVersion('dplyr')}"
))
"x" - Error/problem (red X)"!" - Warning (yellow !)"i" - Information (blue i)"v" - Success (green checkmark)"*" - Bullet point">" - Arrow/pointerFor advanced error design: See references/conditions.md for error design principles, rlang integration, testing strategies, and real-world patterns.
process_data <- function() {
cli_progress_step("Loading data")
data <- load_data()
cli_progress_step("Cleaning data")
clean <- clean_data(data)
cli_progress_step("Analyzing data")
analyze(clean)
}
process_files <- function(files) {
cli_progress_bar("Processing files", total = length(files))
for (file in files) {
process_file(file)
cli_progress_update()
}
}
Progress bars auto-close when the function exits:
process <- function() {
cli_progress_bar("Working", total = 100)
for (i in 1:100) {
Sys.sleep(0.01)
cli_progress_update()
}
# No need to call cli_progress_done() - auto-closes
}
For advanced progress: See references/progress.md for nested progress, custom formats, parallel processing, all progress variables, and Shiny integration.
cli_h1("Main Section")
cli_h2("Subsection")
cli_h3("Detail")
cli_alert_success("Operation completed successfully")
cli_alert_danger("Critical error occurred")
cli_alert_warning("Potential issue detected")
cli_alert_info("Additional information available")
# Regular text with markup
cli_text("This is formatted text with {.emph emphasis}")
# Code blocks
cli_code(c(
"library(dplyr)",
"mtcars %>% filter(mpg > 20)"
))
# Verbatim text (no formatting)
cli_verbatim("This is displayed exactly as-is: {not interpolated}")
# Unordered list
cli_ul()
cli_li("First item")
cli_li("Second item")
cli_end()
# Ordered list
cli_ol()
cli_li("First step")
cli_li("Second step")
cli_end()
# Definition list
cli_dl()
cli_li(c(name = "The name field"))
cli_li(c(email = "The email address"))
cli_end()
# Before: Base R error handling
validate_input <- function(x, y) {
if (!is.numeric(x)) {
stop("x must be numeric")
}
if (length(y) == 0) {
stop("y cannot be empty")
}
if (length(x) != length(y)) {
stop("x and y must have the same length")
}
}
# After: CLI error handling
validate_input <- function(x, y) {
if (!is.numeric(x)) {
cli_abort(c(
"{.arg x} must be numeric",
"x" = "You supplied a {.cls {class(x)}} vector",
"i" = "Use {.fn as.numeric} to convert"
))
}
if (length(y) == 0) {
cli_abort(c(
"{.arg y} cannot be empty",
"i" = "Provide at least one element"
))
}
if (length(x) != length(y)) {
cli_abort(c(
"{.arg x} and {.arg y} must have the same length",
"x" = "{.arg x} has length {length(x)}",
"x" = "{.arg y} has length {length(y)}"
))
}
}
check_required_columns <- function(data, required_cols) {
actual_cols <- names(data)
missing_cols <- setdiff(required_cols, actual_cols)
if (length(missing_cols) > 0) {
cli_abort(c(
"Required column{?s} missing from data",
"x" = "Missing {length(missing_cols)} column{?s}: {.field {missing_cols}}",
"i" = "Data has {length(actual_cols)} column{?s}: {.field {actual_cols}}",
"i" = "Add the missing column{?s} or check for typos"
))
}
invisible(data)
}
process_files <- function(files, verbose = TRUE) {
n <- length(files)
if (verbose) {
cli_progress_bar(
format = "Processing {cli::pb_bar} {cli::pb_current}/{cli::pb_total} [{cli::pb_eta}]",
total = n
)
}
results <- vector("list", n)
for (i in seq_along(files)) {
results[[i]] <- process_file(files[[i]])
if (verbose) {
cli_progress_update()
}
}
results
}
references/inline-markup.md - Complete catalog of inline classes organized by category, advanced patterns, nesting rules, and real-world examples
references/conditions.md - Advanced error design patterns, rlang integration, testing with testthat snapshots, migration guide, and anti-patterns
references/progress.md - Nested progress bars, custom formats, all progress variables, parallel processing, Shiny integration, and debugging
references/themes.md - Complete theming system with CSS-like selectors, container functions, color palettes, custom themes, and accessibility
references/ansi-operations.md - ANSI string operations (align, columns, nchar, etc.), hyperlinks, color detection, testing CLI output, and troubleshooting
{} syntaxtools
Build modern Shiny dashboards and applications using bslib (Bootstrap 5). Use when creating new Shiny apps, modernizing legacy apps (fluidPage, fluidRow/column, tabsetPanel, wellPanel, shinythemes), or working with bslib page layouts, grid systems, cards, value boxes, navigation, sidebars, filling layouts, theming, accordions, tooltips, popovers, toasts, or bslib inputs. Assumes familiarity with basic Shiny.
development
Review test code for quality, design, and completeness after implementing a feature or fixing a bug. Use when the user asks to "review my tests", "check my test quality", "are these tests good enough", "review testing", or after completing a feature implementation that includes tests. Also use when tests feel brittle, flaky, or superficial. Cross-references production code to find coverage gaps.
tools
Guide for drafting issue closure and decline responses as an open-source package maintainer. Use when helping compose a reply that says "no" to a feature request, closes an issue as won't-fix, redirects a user to a different package, explains why a design choice is intentional, or otherwise declines or closes a community contribution. Also use when the maintainer needs to explain a deprecation, point out a user misunderstanding, or communicate an effort/scope tradeoff to a contributor.
tools
R package development with devtools, testthat, and roxygen2. Use when the user is working on an R package, running tests, writing documentation, or building package infrastructure.