Developing Packages R

Community

jeremy-allen

developing-packages-r

Building robust R packages with modern tidyverse patterns. Use this skill when creating or maintaining R packages, designing APIs, choosing dependencies, implementing input validation, writing error messages, or deciding between internal and exported functions. Covers dependency strategy, tidyverse API design patterns, validation approaches, error handling with cli/rlang, testing levels, and documentation priorities.

Overview

Publisher	jeremy-allen
Repository	`claude-skills`
Skill name	`developing-packages-r`
Stars	67
Forks	6
Bundled files	6
Links	Skill source Repository

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.
6 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by jeremy-allen on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/jeremy-allen/claude-skills/tree/main/developing-packages-r
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.

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/jeremy-allen/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/developing-packages-r .claude/skills/developing-packages-r

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Developing Packages R 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 Developing Packages R 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 Developing Packages R 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.

Developing Packages R

This skill covers building robust R packages with modern patterns and best practices.

Dependency Strategy

When to Add Dependencies vs Base R

Add dependency when:

Significant functionality gain
Maintenance burden reduction
User experience improvement
Complex implementation (regex, dates, web)

Use base R when:

Simple utility functions
Package will be widely used (minimize deps)
Dependency is large for small benefit
Base R solution is straightforward

See dependency-decisions.md for example decisions.

Tidyverse Dependency Guidelines

Category	Packages	Guidance
Core (usually worth it)	dplyr, purrr, stringr, tidyr	Complex manipulation, functional programming
Specialized (evaluate carefully)	lubridate, forcats, readr, ggplot2	Only if heavy usage
Heavy (use sparingly)	tidyverse, shiny	Meta-package or interactive apps only

API Design Patterns

Modern Tidyverse API

See api-design.md for:

Use .by for per-operation grouping
Use {{ }} for user-provided columns
Use ... for flexible arguments
Return consistent types (tibbles, not data.frames)

Input Validation Strategy

Validation level depends on function type:

Function Type	Validation Level
User-facing	Comprehensive - check all inputs
Internal	Minimal - assume valid, check invariants
vctrs-based	Type-stable - automatic checking

See validation-patterns.md for examples.

Error Handling Patterns

Good error messages are:

Specific - say what went wrong
Actionable - say how to fix it
Traceable - include function context

See error-handling.md for:

cli package for user-friendly messages
rlang for developer tools
Including function name in errors

Internal vs Exported Functions

Export Function When:

Users will call it directly
Other packages might want to extend it
Part of the core package functionality
Stable API that won't change often

Keep Function Internal When:

Implementation detail that may change
Only used within package
Complex implementation helpers
Would clutter user-facing API

See internal-vs-exported.md for examples.

Testing Strategy

Testing Levels

Level	Purpose	Example
Unit tests	Individual functions	Edge cases, error handling
Integration tests	Workflow combinations	End-to-end pipelines
Property-based tests	Invariants	Function properties hold

See testing-patterns.md for examples.

Documentation Priorities

Must Document:

All exported functions
Complex algorithms or formulas
Non-obvious parameter interactions
Examples of typical usage

Can Skip Documentation:

Simple internal helpers
Obvious parameter meanings
Functions that just call other functions

source: Sarah Johnson's gist https://gist.github.com/sj-io/3828d64d0969f2a0f05297e59e6c15ad

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 Developing Packages R AI skill do?

Why use Developing Packages R on TypingMind?

Because you install it once and use it with any model. Developing Packages R 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 Developing Packages R in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jeremy-allen/claude-skills/tree/main/developing-packages-r. 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 Developing Packages R?

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 Developing Packages R?

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

Is the Developing Packages R AI skill free?

It is published on GitHub by jeremy-allen. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Developing Packages R

Dependency Strategy

When to Add Dependencies vs Base R

Tidyverse Dependency Guidelines

API Design Patterns

Modern Tidyverse API

Input Validation Strategy

Error Handling Patterns

Internal vs Exported Functions

Export Function When:

Keep Function Internal When:

Testing Strategy

Testing Levels

Documentation Priorities

Must Document:

Can Skip Documentation:

Bundled files

Frequently asked questions

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