review-r — community review-r, claude-econ-paper-template, community, ide skills

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 imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

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.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

review-r

Install review-r, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
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]

Related Skills

Looking for an alternative to review-r or another community skill for your workflow? Explore these related open-source skills.

View All

openclaw-release-maintainer

Logo of openclaw
openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

333.8k
0
AI

widget-generator

Logo of f
f

Generate customizable widget plugins for the prompts.chat feed system

149.6k
0
AI

flags

Logo of vercel
vercel

The React Framework

138.4k
0
Browser

pr-review

Logo of pytorch
pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

98.6k
0
Developer