review-r — community review-r, claude-econ-paper-template, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0

About this Skill

Ideal for Data Science Agents focused on empirical economics research needing automated R script validation. Code review for R scripts checking reproducibility, correctness, and conventions

naj2r naj2r
[0]
[0]
Updated: 3/5/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reviewed Landing Page Review Score: 9/11

Killer-Skills keeps this page indexable because it adds recommendation, limitations, and review signals beyond the upstream repository text.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review Locale and body language aligned
Review Score
9/11
Quality Score
54
Canonical Locale
en
Detected Body Locale
en

Ideal for Data Science Agents focused on empirical economics research needing automated R script validation. Code review for R scripts checking reproducibility, correctness, and conventions

Core Value

Empowers agents to ensure reproducibility and correctness in R scripts or QMD chapters by checking for set.seed() presence, absolute paths, and deterministic data loading, leveraging libraries and protocols like library() calls and caching for web scraping.

Ideal Agent Persona

Ideal for Data Science Agents focused on empirical economics research needing automated R script validation.

Capabilities Granted for review-r

Validating R script reproducibility for research papers
Debugging QMD chapters for empirical economics studies
Automating code reviews for data loading and library calls

! Prerequisites & Limits

  • Requires access to .R or .qmd files
  • Limited to R scripts and QMD chapters
  • Does not support web scraping without caching

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is review-r?

Ideal for Data Science Agents focused on empirical economics research needing automated R script validation. Code review for R scripts checking reproducibility, correctness, and conventions

How do I install review-r?

Run the command: npx killer-skills add naj2r/claude-econ-paper-template/review-r. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for review-r?

Key use cases include: Validating R script reproducibility for research papers, Debugging QMD chapters for empirical economics studies, Automating code reviews for data loading and library calls.

Which IDEs are compatible with review-r?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for review-r?

Requires access to .R or .qmd files. Limited to R scripts and QMD chapters. Does not support web scraping without caching.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add naj2r/claude-econ-paper-template/review-r. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use review-r immediately in the current project.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

review-r

Install review-r, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

Review R Code

Run a code review protocol on an R script or QMD chapter.

Input: $ARGUMENTS — path to .R or .qmd file.

Review Dimensions

1. Reproducibility

  • set.seed() present if any randomness used
  • All paths are absolute or use defined root variable
  • library() calls at top (not scattered)
  • No hardcoded values that should be variables
  • Data loading is deterministic (no web scraping without caching)

2. Correctness

  • Regression specification matches paper ({{unit_fe}}+{{time_fe}} FE, clustered SEs)
  • Missing value handling is explicit (na.rm=TRUE where needed)
  • Joins preserve expected row counts (check for accidental duplication)
  • Factor/character conversions are intentional
  • Variable names match Stata equivalents for cross-verification

3. Conventions (from .claude/rules/stata-r-conventions.md)

  • library() not require()
  • |> preferred over %>%
  • fixest::feols() for TWFE
  • modelsummary for tables
  • haven::read_dta() for Stata files
  • snake_case naming
  • Comments explain non-obvious logic

4. Quarto-Specific (if .qmd)

  • Every code chunk has a unique label:
  • echo: and eval: set appropriately
  • Table/figure chunks have captions (tbl-cap:, fig-cap:)
  • Cross-references use @sec-, @tbl-, @fig- syntax
  • No orphaned code chunks (every chunk has surrounding narrative)

Output

Report by severity (do NOT edit files, report only):

  • Error: Will produce wrong results or fail to run
  • Warning: May produce unexpected behavior
  • Style: Convention violation, should fix for consistency
  • Note: Suggestion for improvement

Format:

[SEVERITY] Line [N]: [Description]
  Found:    [what's there]
  Expected: [what should be there]

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