review-r

Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files.

Ir para a instalação

Repositório: brycewang-stanford/Auto-Empirical-Research-Skills
Última atividade na origem: 3 de abril de 2026 às 02:07
Idioma detectado do SKILL.md: inglês
Estrelas: 3.291
Forks: 432

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.

npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill review-r

O comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.

Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

SKILL.md

Instruções da origem · Visualização somente leitura

Voltar à pesquisa Voltar à instalação

auto-empirical-research-skills

Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.

2026-08-06

de-aigc-skills

中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.

2026-08-04

kaggle-research

Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.

2026-07-31

Ver todas deste repositório

name	review-r
description	Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files.
disable-model-invocation	true
argument-hint	[filename or 'all']
allowed-tools	["Read","Grep","Glob","Write","Task"]

Review R Code

Run a comprehensive R code review on the specified script(s). Do NOT edit any R source files -- produce a report only.

Steps

Identify target: Use $ARGUMENTS to find the R file(s). If all, scan all .R files in scripts/ and project subdirectories.
Launch r-reviewer agent following the full protocol from .claude/agents/r-reviewer.md.
Read standards from .claude/rules/r-code-conventions.md.
Check 10 categories: Structure & Header, Console Output Hygiene, Reproducibility, Function Design, Domain Correctness, Figure Quality, RDS Data Pattern, Comment Quality, Error Handling, Professional Polish.
Save report to quality_reports/[script_name]_r_review.md.
Present summary: Total issues per script, severity breakdown (Critical/High/Medium/Low), top 3 critical issues.

Important

NEVER edit source files. Report only.
Prioritize correctness over style.

review-r

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Mais deste repositório

Review R Code

Steps

Important

Review R Code

Steps

Important