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Review r

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/15-Felpix-Studios-social-science-research/skills/review-r

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Install
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-r

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

What its author says it does

Copied from the file, not written here

Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files. Make sure to use this skill whenever the user wants their existing R code evaluated or audited — not when they want new analysis written. Triggers include: "review my R script", "check my R code", "is my code replication-ready", "audit this R file", "does this code follow conventions", "will this reproduce", "check my analysis script", "code review", "review-r", or when the user has an existing .R file and wants quality feedback rather than new code.

SKILL.md

2.1 KB, as published. Nobody here has run it

Review R Scripts

Run the comprehensive R code review protocol.

Steps

  1. Identify scripts to review:

    • If $ARGUMENTS is a specific .R filename: review that file only
    • If $ARGUMENTS is a name pattern (e.g., model_name): glob for matching .R files. If multiple matches, use AskUserQuestion:
      • header: "Scripts"
      • question: "Multiple R scripts match that pattern. Which should I review?"
      • multiSelect: true
      • options: list up to 4 matched files (label: filename, description: path and last modified). User can select multiple.
    • If $ARGUMENTS is all: review all R scripts in scripts/R/ and Figures/*/
    • If $ARGUMENTS is empty, glob for all .R files. If multiple found, use AskUserQuestion as above.

  2. For each script, launch the r-reviewer agent with instructions to:

    • Follow the full protocol in the agent instructions
    • Read rules/r-code-conventions.md for current standards
    • Save report to quality_reports/[script_name]_r_review.md

  3. After all reviews complete, present a summary:

    • Total issues found per script
    • Breakdown by severity (Critical / High / Medium / Low)
    • Top 3 most critical issues

  4. IMPORTANT: Do NOT edit any R source files. Only produce reports. Fixes are applied after user review.

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