# Econ Visualization

Create publication-quality charts and graphs for economics papers.

- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econ-visualization -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econ-visualization -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econ-visualization -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econ-visualization -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills`

## Description

Create publication-quality charts and graphs for economics papers.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Econ Visualization

## Purpose

This skill creates publication-quality figures for economics papers, using clean styling, consistent scales, and export-ready formats.

## When to Use

- Building figures for empirical results and descriptive analysis
- Standardizing chart style across a paper or presentation
- Exporting figures to PDF or PNG at journal quality

## Instructions

Follow these steps to complete the task:

### Step 1: Understand the Context

Before generating any code, ask the user:

- What is the dataset and key variables?
- What chart type is needed (line, bar, scatter, event study)?
- What output format and size are required?

### Step 2: Generate the Output

Based on the context, generate code that:

1. **Uses a consistent theme** for academic styling
2. **Labels axes and legends clearly**
3. **Exports figures** at high resolution
4. **Includes reproducible steps** for data preparation

### Step 3: Verify and Explain

After generating output:

- Explain how to regenerate or update the plot
- Suggest alternatives (log scales, faceting, smoothing)
- Note any data transformations used

## Example Prompts

- "Create an event study plot with confidence intervals"
- "Plot GDP per capita over time for three countries"
- "Build a scatter plot with fitted regression line"

## Example Output

```r
# ============================================
# Publication-Quality Figure in R
# ============================================
library(tidyverse)

df <- read_csv("data.csv")

ggplot(df, aes(x = year, y = gdp_per_capita, color = country)) +
  geom_line(size = 1) +
  scale_y_continuous(labels = scales::comma) +
  labs(
    title = "GDP per Capita Over Time",
    x = "Year",
    y = "GDP per Capita (USD)",
    color = "Country"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  )

ggsave("figures/gdp_per_capita.pdf", width = 7, height = 4, dpi = 300)
```

## Requirements

### Software

The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/communication/econ-visualization/SKILL.md