R Cli App logo

R Cli App

Organization
posit-dev
r-cli-app

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.

Overview

Publisherposit-dev
Repositoryskills
Skill namer-cli-app
Stars
516
Forks
53
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by posit-dev on GitHub. Read the source before you install it.

Installation

Install the R Cli App AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/posit-dev/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/r-lib/r-cli-app .claude/skills/r-cli-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable R Cli App in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads R Cli App on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so R Cli App is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Building CLI Apps with Rapp

Rapp (v0.3.0) is an R package that provides a drop-in replacement for Rscript that automatically parses command-line arguments into R values. It turns simple R scripts into polished CLI apps with argument parsing, help text, and subcommand support — with zero boilerplate.

R ≥ 4.1.0 | CRAN: install.packages("Rapp") | GitHub: r-lib/Rapp

After installing, put the Rapp launcher on PATH:

r
Rapp::install_pkg_cli_apps("Rapp")

This places the Rapp executable in ~/.local/bin (macOS/Linux) or %LOCALAPPDATA%\Programs\R\Rapp\bin (Windows).


Core Concept: Scripts Are the Spec

Rapp scans top-level expressions of an R script and converts specific patterns into CLI constructs. This means:

  1. The same script works identically via source() and as a CLI tool.
  2. You write normal R code — Rapp infers the CLI from what you write.
  3. Default values in your R code become the CLI defaults.

Only top-level assignments are recognized. Assignments inside functions, loops, or conditionals are not parsed as CLI arguments.


Pattern Recognition: R → CLI Mapping

This table is the heart of Rapp — each R pattern automatically maps to a CLI surface:

R Top-Level ExpressionCLI SurfaceNotes
foo <- "text"--foo <value>String option
foo <- 1L--foo <int>Integer option
foo <- 3.14--foo <float>Float option
foo <- TRUE / FALSE--foo / --no-fooBoolean toggle
foo <- NA_integer_--foo <int>Optional integer (NA = not set)
foo <- NA_character_--foo <str>Optional string (NA = not set)
foo <- NULLpositional argRequired by default
foo... <- NULLvariadic positionalZero or more values
foo <- c()repeatable --fooMultiple values as strings
foo <- list()repeatable --fooMultiple values parsed as YAML/JSON
switch("", cmd1={}, cmd2={})subcommandsapp cmd1, app cmd2
switch(cmd <- "", ...)subcommandsSame; captures command name in cmd

Type behavior

  • Non-string scalars are parsed as YAML/JSON at the CLI and coerced to the R type of the default. n <- 5L means --n 10 gives integer 10L.
  • NA defaults signal optional arguments. Test with !is.na(myvar).
  • Snake case variable names map to kebab-case: n_flips--n-flips.
  • Positional args always arrive as character strings — convert manually.

Script Structure

Shebang line

r
#!/usr/bin/env Rapp

Makes the script directly executable on macOS/Linux after chmod +x. On Windows, call Rapp myscript.R explicitly.

Front matter metadata

Hash-pipe comments (#|) before any code set script-level metadata:

r
#!/usr/bin/env Rapp
#| name: my-app
#| title: My App
#| description: |
#|   A short description of what this app does.
#|   Can span multiple lines using YAML block scalar `|`.

The name: field sets the app name in help output (defaults to filename).

Per-argument annotations

Place #| comments immediately before the assignment they annotate:

r
#| description: Number of coin flips
#| short: 'n'
flips <- 1L

Available annotation fields:

FieldPurpose
description:Help text shown in --help
title:Display title (for subcommands and front matter)
short:Single-letter alias, e.g. 'n'-n
required:true/false — for positional args only
val_type:Override type: string, integer, float, bool, any
arg_type:Override CLI type: option, switch, positional
action:For repeatable options: replace or append

Add #| short: for frequently-used options — users expect single-letter shortcuts for common flags like verbose (-v), output (-o), or count (-n).


Named Options

Scalar literal assignments become named options:

r
name <- "world"          # --name <value>    (string, default "world")
count <- 1L              # --count <int>     (integer, default 1)
threshold <- 0.5         # --threshold <flt> (float, default 0.5)
seed <- NA_integer_      # --seed <int>      (optional, NA if omitted)
output <- NA_character_  # --output <str>    (optional, NA if omitted)

For optional arguments, test whether the user supplied them:

r
seed <- NA_integer_
if (!is.na(seed)) set.seed(seed)

Boolean Switches

TRUE/FALSE assignments become toggles:

r
verbose <- FALSE   # --verbose or --no-verbose
wrap <- TRUE       # --wrap (default) or --no-wrap

Values yes/true/1 set TRUE; no/false/0 set FALSE.

Repeatable Options

r
pattern <- c()     # --pattern '*.csv' --pattern 'sales-*'  → character vector
threshold <- list() # --threshold 5 --threshold '[10,20]'   → list of parsed values

Positional Arguments

Assign NULL for positional args (required by default):

r
#| description: The input file to process.
input_file <- NULL

Make optional with #| required: false. Test with is.null(myvar).

Variadic positional args

Use ... suffix to collect multiple positional values:

r
pkgs... <- c()
# install-pkgs dplyr ggplot2 tidyr → pkgs... = c("dplyr", "ggplot2", "tidyr")

Subcommands

Use switch() with a string first argument to declare subcommands. Options before the switch() are global; options inside branches are local to that subcommand.

r
switch(
  command <- "",

  #| title: Display the todos
  list = {
    #| description: Max entries to display (-1 for all).
    limit <- 30L
    # ... list implementation
  },

  #| title: Add a new todo
  add = {
    #| description: Task description to add.
    task <- NULL
    # ... add implementation
  },

  #| title: Mark a task as completed
  done = {
    #| description: Index of the task to complete.
    index <- 1L
    # ... done implementation
  }
)

Help is scoped: myapp --help lists commands; myapp list --help shows list-specific options plus globals. Subcommands can nest by placing another switch() inside a branch.


Built-in Help

Every Rapp automatically gets --help (human-readable) and --help-yaml (machine-readable). These work with subcommands too.


Development and Testing

Interactive Development

Use Rapp::run() to test scripts from an R session:

r
Rapp::run("path/to/myapp.R", c("--help"))
Rapp::run("path/to/myapp.R", c("--name", "Alice", "--count", "5"))

It returns the evaluation environment (invisibly) for inspection, and supports browser() for interactive debugging.

Testing CLI Apps in Packages

Use Rapp::run() with testthat snapshot testing. Test computed values by accessing the returned environment, and test output with expect_snapshot().

See references/advanced.md for detailed testing patterns, including:

  • Accessing computed values via the evaluation environment
  • Snapshot testing for help output and formatted text
  • Testing file side effects and state changes

Complete Example: Coin Flipper

r
#!/usr/bin/env Rapp
#| name: flip-coin
#| description: |
#|   Flip a coin.

#| description: Number of coin flips
#| short: 'n'
flips <- 1L

sep <- " "
wrap <- TRUE

seed <- NA_integer_
if (!is.na(seed)) {
  set.seed(seed)
}

cat(sample(c("heads", "tails"), flips, TRUE), sep = sep, fill = wrap)
sh
flip-coin            # heads
flip-coin -n 3       # heads tails heads
flip-coin --seed 42 -n 5
flip-coin --help

Generated help:

Usage: flip-coin [OPTIONS]

Flip a coin.

Options:
  -n, --flips <FLIPS>  Number of coin flips [default: 1] [type: integer]
      --sep <SEP>      [default: " "] [type: string]
      --wrap / --no-wrap  [default: true]
      --seed <SEED>    [default: NA] [type: integer]

Complete Example: Todo Manager (Subcommands)

r
#!/usr/bin/env Rapp
#| name: todo
#| description: Manage a simple todo list.

#| description: Path to the todo list file.
#| short: s
store <- ".todo.yml"

switch(
  command <- "",

  list = {
    #| description: Max entries to display (-1 for all).
    limit <- 30L

    tasks <- if (file.exists(store)) yaml::read_yaml(store) else list()
    if (!length(tasks)) {
      cat("No tasks yet.\n")
    } else {
      if (limit >= 0L) tasks <- head(tasks, limit)
      writeLines(sprintf("%2d. %s\n", seq_along(tasks), tasks))
    }
  },

  add = {
    #| description: Task description to add.
    task <- NULL

    tasks <- if (file.exists(store)) yaml::read_yaml(store) else list()
    tasks[[length(tasks) + 1L]] <- task
    yaml::write_yaml(tasks, store)
    cat("Added:", task, "\n")
  },

  done = {
    #| description: Index of the task to complete.
    #| short: i
    index <- 1L

    tasks <- if (file.exists(store)) yaml::read_yaml(store) else list()
    task <- tasks[[as.integer(index)]]
    tasks[[as.integer(index)]] <- NULL
    yaml::write_yaml(tasks, store)
    cat("Completed:", task, "\n")
  }
)
sh
todo add "Write quarterly report"
todo list
todo list --limit 5
todo done 1
todo --store /tmp/work.yml list

Shipping CLIs in an R Package

Place CLI scripts in exec/ and add Rapp to Imports in DESCRIPTION:

mypkg/
├── DESCRIPTION
├── R/
├── exec/
│   ├── myapp       # script with #!/usr/bin/env Rapp shebang
│   └── myapp2
└── man/

Users install the CLI launchers after installing the package:

r
Rapp::install_pkg_cli_apps("mypkg")

Expose a convenience installer so users don't need to know about Rapp:

r
#' Install mypkg CLI apps
#' @export
install_mypkg_cli <- function(destdir = NULL) {
  Rapp::install_pkg_cli_apps(package = "mypkg", destdir = destdir)
}

By default, launchers set --default-packages=base,<pkg>, so only base and the package are auto-loaded. Use library() for other dependencies.


Quick Reference: Common Patterns

NA vs NULL for optional arguments

  • NA (NA_integer_, NA_character_) → optional named option. Test: !is.na(x).
  • NULL + #| required: false → optional positional arg. Test: !is.null(x).

stdin/stdout

r
input_file <- NA_character_
con <- if (is.na(input_file)) file("stdin") else file(input_file, "r")
lines <- readLines(con)
writeLines(lines, stdout())

Exit codes and stderr

r
message("Error: something went wrong")   # writes to stderr
cat("Error:", msg, "\n", file = stderr()) # also stderr
quit(status = 1)                          # non-zero exit

Error handling

r
tryCatch({
  result <- do_work()
}, error = function(e) {
  cat("Error:", conditionMessage(e), "\n", file = stderr())
  quit(status = 1)
})

Additional Reference

For less common topics — launcher customization (#| launcher: front matter), detailed Rapp::install_pkg_cli_apps() API options, and more complete examples (deduplication filter, variadic install-pkg, interactive fallback) — read references/advanced.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the R Cli App AI skill do?

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.

Why use R Cli App on TypingMind?

Because you install it once and use it with any model. R Cli App is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install R Cli App in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/posit-dev/skills/tree/main/r-lib/r-cli-app. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use R Cli App?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with R Cli App?

As many as you like. As long as a model supports skills, you can use R Cli App with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the R Cli App AI skill free?

Yes. It is published on GitHub by posit-dev under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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