rlang-conditions

Implement R package error handling with rlang conditions and testthat snapshots.

13|2|Updated Jan 13, 2026
One-click install
npx skills add https://github.com/jsperger/llm-r-skills --skill rlang-conditions
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rlang-conditions
Source: https://github.com/jsperger/llm-r-skills/tree/main/skills/rlang-conditions
Command: npx skills add https://github.com/jsperger/llm-r-skills --skill rlang-conditions

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps R developers implement robust error handling in packages, enabling consistent, user-friendly error reporting and reliable failure handling.

Core Features & Use Cases

  • Formatted conditions: Use cli_abort(), cli_warn(), and cli_inform() to craft informative error and status messages.
  • Error context: Attach contextual information with caller_env() and caller_arg() so end users see meaningful call traces.
  • Input validation: Provide reusable checkers that validate arguments and report precise function and parameter names.
  • Error chaining: Use try_fetch() to wrap operations and chain contextual errors without losing the original cause.
  • Testing: Validate error behaviour with testthat snapshots to ensure snapshots reflect produced messages.

Quick Start

Load the rlang package in your package development workflow and replace base error calls with cli_abort/cli_warn/cli_inform. For example, enforce argument types with a helper that uses caller_arg() to display the correct parameter name, and wrap risky operations with try_fetch() to preserve context.

Frequently Asked Questions about rlang-conditions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement robust R error handling in my package?

Robust R error handling uses cli_abort() and cli_warn() to produce formatted, contextual conditions. It replaces base error calls to generate user-friendly messages and consistent failure reporting within package development workflows.

How does try_fetch() chain errors in R without losing context?

try_fetch() chains errors in R by wrapping risky operations to capture and attach contextual information. It preserves the original cause of failures while adding new context, preventing error masking during package execution.

Can I display correct parameter names during R input validation?

Input validation in R displays correct parameter names by using caller_arg() and caller_env(). These helpers identify the exact function and parameter source, reporting precise argument names in the resulting error messages.

Does this error handling approach work with testthat snapshots?

Yes, this error handling approach works with testthat snapshots to validate error behavior. It integrates with standard testing workflows, ensuring snapshots accurately reflect the formatted messages produced by cli_abort and related functions.

What R and rlang versions are needed for cli_abort and caller_env?

Using cli_abort and caller_env for error handling requires R version 4.3 or higher and rlang version 1.1.3 or higher. These versions provide the necessary condition formatting and environment tracing capabilities.