菜单
End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill data-analysis命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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.
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.
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.
基于 SOC 职业分类
正在显示 SKILL.md
| name | data-analysis |
| description | End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures. |
| disable-model-invocation | true |
| argument-hint | [dataset path or description of analysis goal] |
| allowed-tools | ["Read","Grep","Glob","Write","Edit","Bash","Task"] |
Run an end-to-end data analysis in R through 5 phases.
.claude/rules/r-code-conventions.mdlibrary() (never require())set.seed() if stochastic elements are involvedoutput/diagnostics/fixest for panel data with fixed effects (see /econometrics-r for detailed patterns)lm/glm for cross-section/causal-inference-r for diagnostics and modern estimators/econometrics-r for spdep/spatialreg patternsmodelsummary (preferred) or stargazer, export as .tex and .htmlggplot2 with project theme, explicit dimensions, white background, 300 DPI.pdf and .pngsaveRDS() for all computed objects# ==============================================================================
# Title: [Analysis Name]
# Author: [Name]
# Date: [YYYY-MM-DD]
# Purpose: [What this script does]
# Inputs: [Data files]
# Outputs: [Tables, figures, RDS files]
# ==============================================================================
# 0. Setup ----
library(tidyverse)
library(fixest)
library(modelsummary)
# 1. Data Loading ----
# 2. Exploratory Analysis ----
# 3. Main Analysis ----
# 4. Tables and Figures ----
# 5. Export ----
All scripts saved to scripts/R/, all outputs to output/.