What problem does it solve? R functions often fail with cryptic errors when users pass invalid arguments. This Skill guides you through tidyverse-style input validation using rlang's standalone check_* functions, producing clear, actionable error messages at function entry points. ## Core Features & Use Cases - Scalar and Vector Checkers: Use check_string(), check_number_whole(), check_bool(), check_logical(), check_character(), and check_data_frame() to validate argument types with options like min, max, allow_na, and allow_null. - Argument Helpers: Apply arg_match() for enumerated choices, check_exclusive() for mutually exclusive arguments, and check_required() for mandatory parameters. - Custom Check Functions: Build your own check_* wrappers that correctly propagate arg and call context, or create new validators with stop_input_type(). - Use Case: When writing an exported R package function like create_report(title, n_rows), validate inputs at the entry point so internal helpers can trust the data without redundant checks. ## Quick Start Add tidyverse-style input validation to my exported R function using rlang check functions.
