Validate R function inputs with standalone check_* helper files.
npx skills add https://github.com/r-lib/cpp11 --skill types-check
Please help me install this Agent Skill. Skill: types-check Source: https://github.com/r-lib/cpp11/tree/main/.claude/skills/tidy-argument-checking Command: npx skills add https://github.com/r-lib/cpp11 --skill types-check
This Skill provides a centralized, opinionated approach to validating function inputs in R by using a standalone file of check_* helpers. It helps packages enforce consistent, clear error messages at the boundary between user code and internal logic, reducing boilerplate.
Use the tidy-argument-checking approach to add a standalone file of check_* helpers to your package, then validate inputs at the start of exported functions.
High-intent search queries and answers about installing and using this skill.
To validate function inputs in R with consistent error messages, use a standalone file of check_* helpers like check_string and check_data_frame at the start of exported functions, enforcing early boundary validation and reducing boilerplate code.
The tidy-argument-checking approach for R package development involves adding a standalone file of scalar and vector check_* helpers to validate inputs at the entry point of exported functions, ensuring clear and consistent error reporting.
You can check if an R function input is a string or a whole number by applying scalar validators like check_string and check_number_whole, which verify input types and return standardized error messages if validation fails.
Yes, you can allow NULL or NA values when validating data frames in R by using the allow_null and allow_na arguments within vector validators like check_data_frame to handle nuanced missing data scenarios.
To propagate caller context when wrapping input validation checks in R, pass the arg and call contexts through your custom check_ functions, preserving accurate error reporting back to the original user code boundary.
No, standalone input validation does not require external dependencies for R packages because the check_* helpers are implemented within a single standalone file, keeping your package lightweight and modular without adding external dependencies.
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