Econ Visualization
- 1 installs
- 3.2k repo stars
- Updated August 4, 2026
- brycewang-stanford/awesome-agent-skills-for-empirical-research
econ-visualization is a Claude Code skill that generates publication-quality charts and graphs for economics papers using ggplot2 or matplotlib.
About
This skill generates publication-quality charts and graphs for economics papers. A researcher uses it to build line, bar, scatter, and event-study figures with consistent academic styling and export-ready PDF/PNG output. It defaults to R and ggplot2 with matplotlib/seaborn as a Python alternative.
- Generates publication-quality economics figures with a consistent academic theme
- Exports vector PDF/PNG at journal resolution (300 dpi)
- R (ggplot2) primary, Python matplotlib/seaborn optional
Econ Visualization by the numbers
- 1 all-time installs (skills.sh)
- Ranked #1,803 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
econ-visualization capabilities & compatibility
- Capabilities
- data visualization · event study plot · chart styling
- Use cases
- data analysis · research
What econ-visualization says it does
This skill creates publication-quality figures for economics papers, using clean styling, consistent scales, and export-ready formats.
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| Installs | 1 |
|---|---|
| repo stars | ★ 3.2k |
| Last updated | August 4, 2026 |
| Repository | brycewang-stanford/awesome-agent-skills-for-empirical-research ↗ |
What it does
Create clean, journal-ready charts (event study, scatter with fitted line, time series) for an economics paper.
Who is it for?
Standardizing figure style across an economics paper and exporting at journal quality
Skip if: General non-academic dashboards or interactive web charts
When should I use this skill?
Building figures for empirical results or standardizing chart style across a paper
What you get
- Publication-ready figure code
- Exported PDF/PNG figures
By the numbers
- 3-step workflow (understand context, generate output, verify and explain)
Files
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
# ============================================
# 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
Changelog
v1.0.0
- Initial release
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
# ============================================
# 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
Changelog
v1.0.0
- Initial release
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What languages does this skill use?
R 4.0+ (ggplot2, scales, dplyr) primarily, with Python matplotlib/seaborn as an optional alternative.
What output formats does it produce?
It exports figures to PDF or PNG at high resolution, favoring vector formats like PDF or SVG for publication.