Create publication-quality charts and graphs for economics papers.
Scanned 9/3/2026
Install to Claude Code
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econ-visualization --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: econ-visualization
description: Create publication-quality charts and graphs for economics papers.
workflow_stage: communication
compatibility:
- claude-code
- cursor
- codex
- gemini-cli
author: Awesome Econ AI Community
version: 1.0.0
tags:
- visualization
- ggplot2
- charts
- publication
---
# 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
- R 4.0+ or Python 3.10+
### Packages
- For R: `ggplot2`, `scales`, `dplyr`
- For Python: `matplotlib`, `seaborn` (optional alternative)
## Best Practices
1. **Use vector formats** (PDF, SVG) for publication
2. **Keep labels concise** and readable
3. **Document data filters** used in the figure
## Common Pitfalls
- Overcrowded plots without clear labeling
- Inconsistent scales across figures
- Exporting low-resolution images
## References
- [ggplot2 documentation](https://ggplot2.tidyverse.org/)
- [Tufte (2001) The Visual Display of Quantitative Information](https://www.edwardtufte.com/tufte/books_vdqi)
## Changelog
### v1.0.0
- Initial release
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