Generate journal-ready econometrics figures in Python, R, and Stata.
npx skills add https://github.com/sheehe/coase --skill figure
Please help me install this Agent Skill. Skill: figure Source: https://github.com/sheehe/coase/tree/main/%E5%AE%9E%E8%AF%81%E7%A7%91%E7%A0%94%E6%8F%92%E4%BB%B6/econometrics/econometrics/skills/figure Command: npx skills add https://github.com/sheehe/coase --skill figure
Econometrics researchers struggle to produce publication-quality figures that meet journal formatting standards, consuming valuable time on typography, sizing, and export settings.
Tell me the figure type and data you want to visualize, and I will generate a publication-ready econometrics figure ready for journal submission.
High-intent search queries and answers about installing and using this skill.
Publication-quality econometrics figures apply journal-ready defaults for fonts, line widths, dimensions, and color palettes. You can generate export-ready vector PDFs for event-study plots, coefficient plots, and binscatter by using enforced journal formatting standards.
Yes, you can generate event-study plots and RDD visualizations across Python, R, and Stata workflows. The templates provide cross-language code for consistent figure generation using matplotlib and ggplot2.
The best way to format figures for top economics journals is to apply enforced journal standards for typography, sizing, and grayscale-safe color palettes. This ensures your matplotlib and ggplot2 outputs meet strict journal submission formatting requirements.
No, you do not need to manually configure vector export settings. The templates enforce journal standards for vector PDF export and grayscale-safe palettes automatically, reducing setup time for your econometric figures.
Yes, this solution supports multi-panel time-series and density plots in Stata. It provides ready-to-use code templates and best-practice notes for these common econometric figure types alongside Python and R workflows.
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