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review-r

Automate quality review of R scripts and generate non-destructive reports.

Updated Aug 27, 2026
Search Tags:#reporting#quality#r#static-analysis#code-review#reproducibility
One-click install
npx skills add https://github.com/diegosolarfuentes/geopolitics-gvc --skill review-r-diegosolarfuentes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-r
Source: https://github.com/diegosolarfuentes/geopolitics-gvc/tree/main/.claude/skills/review-r
Command: npx skills add https://github.com/diegosolarfuentes/geopolitics-gvc --skill review-r-diegosolarfuentes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The R code review protocol helps teams ensure code quality, reproducibility, and adherence to domain standards by producing structured review reports without modifying source files.

Core Features & Use Cases

  • Automated Review Protocol: Runs a standardized set of checks on R scripts to surface issues related to style, reproducibility, and correctness.
  • Non-destructive Reporting: Generates a comprehensive report without editing source files, suitable for audits and reviews.
  • Use Case: Data science teams can run the protocol on project scripts to generate a single quality report for release readiness.

Quick Start

Install and run the r-reviewer pipeline on your R scripts to generate a quality report.

Frequently Asked Questions about review-r

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate code review for R scripts to check reproducibility and quality?▼

You can automate R script code review by running a standardized review protocol that checks style, reproducibility, and correctness. It generates a detailed, non-destructive quality report saved as a markdown file for release readiness audits.

Can I run a static analysis quality check on specific R files instead of the entire project?▼

Yes, static analysis quality checks can be run on R projects of any size. You can select specific R files for review or process all scripts, generating individual markdown reports for each evaluated script.

Does automated R code review modify the original source files during analysis?▼

No, automated R code review is non-destructive. It analyzes your scripts and produces a comprehensive quality report in a separate markdown file without editing or modifying your original source code.

What is the best way to generate reproducibility reports for R data science scripts?▼

The best way to generate reproducibility reports is using an automated review protocol that surfaces domain standard issues. It outputs a structured, non-destructive markdown report detailing code quality and correctness without altering source files.

Where are the quality review reports stored after checking R scripts?▼

Quality review reports for R scripts are stored in a dedicated directory. The detailed markdown reports are saved to quality_reports/[script_name]_r_review.md, ensuring organized tracking of code review results.

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