r-oop Skill
description: R object-oriented programming guide for S7, S3, S4, and vctrs. Use when designing R classes or choosing an OOP system.
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Why use this skill
r-oop is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Claude Code Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in ab604/claude-code-r-skills, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: r-oop
description: R object-oriented programming guide for S7, S3, S4, and vctrs. Use when designing R classes or choosing an OOP system.
---
# R Object-Oriented Programming
*S7, S3, S4, and vctrs: choosing the right OOP system for your needs*
## S7: Modern OOP for New Projects
- **S7 combines S3 simplicity with S4 structure**
- **Formal class definitions with automatic validation**
- **Compatible with existing S3 code**
```r
# S7 class definition
Range <- new_class("Range",
properties = list(
start = class_double,
end = class_double
),
validator = function(self) {
if (self@end < self@start) {
"@end must be >= @start"
}
}
)
# Usage - constructor and property access
x <- Range(start = 1, end = 10)
x@start # 1
x@end <- 20 # automatic validation
# Methods
inside <- new_generic("inside", "x")
method(inside, Range) <- function(x, y) {
y >= x@start & y <= x@end
}
```
## OOP System Decision Matrix
### S7 vs vctrs vs S3/S4 Decision Tree
**Start here:** What are you building?
### 1. Vector-like objects (things that behave like atomic vectors)
```
Use vctrs when:
- Need data frame integration (columns/rows)
- Want type-stable vector operations
- Building factor-like, date-like, or numeric-like classes
- Need consistent coercion/casting behavior
- Working with existing tidyverse infrastructure
Examples: custom date classes, units, categorical data
```
### 2. General objects (complex data structures, not vector-like)
```
Use S7 when:
- NEW projects that need formal classes
- Want property validation and safe property access (@)
...