Agent skill · Documentation

tidyverse-patterns

Modern tidyverse patterns for R including pipes, joins, grouping, purrr, and stringr. Use when writing tidyverse R code.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeNOASSERTION
Install
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill tidyverse-patterns --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 12 KB
Bundled scripts: none
Path: skills/55-ab604-claude-code-r-skills/skills/tidyverse-patterns/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 3,244
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Modern Tidyverse Patterns *Best practices for modern tidyverse development with dplyr 1.1+ and R 4.3+* ## Core Principles 1. **Use modern tidyverse patterns** - Prioritize dplyr 1.1+ features, native pipe, and current APIs 2. **Profile before optimizing** - Use profvis and bench to identify real bottlenecks 3. **Write readable code first** - Optimize only when necessary and after profiling 4. **

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About this skill
What does the tidyverse-patterns skill do?

Modern tidyverse patterns for R including pipes, joins, grouping, purrr, and stringr. Use when writing tidyverse R code.

How do I install it?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill tidyverse-patterns --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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