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figure-design

Design publication-quality scientific figures in Python and R.

6|2|Updated Jun 11, 2026
Search Tags:#python#r#publication quality#scientific visualization#figure design
One-click install
npx skills add https://github.com/pradyumnasagar/open-research-skills --skill figure-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: figure-design
Source: https://github.com/pradyumnasagar/open-research-skills/tree/main/skills/scientific-visualization/figure-design
Command: npx skills add https://github.com/pradyumnasagar/open-research-skills --skill figure-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps researchers create publication-quality figures in Python (matplotlib, seaborn, plotly) and R (ggplot2), ensuring they meet journal standards and accessibility guidelines.

Core Features & Use Cases

  • Figure Design in Python and R: Offers guidelines for creating figures in both languages.
  • Best Practices: Covers DPI, vector vs raster, color-blind safe palettes, fonts, and multi-panel layout.
  • Use Case: If you need to produce figures for a journal article or presentation, this Skill provides detailed instructions and code examples to ensure your figures are of the highest quality.

Quick Start

Execute the skill with the command 'design_figure' and provide the necessary input files and parameters.

Frequently Asked Questions about figure-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-quality scientific figures in Python and R?▼

You can create publication-quality scientific figures using this Skill, which provides detailed instructions and code examples for matplotlib, seaborn, plotly in Python, and ggplot2 in R to ensure your visualizations meet strict journal standards.

What are the best practices for scientific visualization formatting and accessibility?▼

Best practices for scientific visualization include using appropriate DPI, choosing between vector and raster formats, applying color-blind safe palettes, selecting readable fonts, and structuring clear multi-panel layouts to meet accessibility guidelines.

Do I need to know Python and R to use this Skill for figure design?▼

Yes, you need existing knowledge of Python and R for figure manipulation and visualization, as this Skill focuses on providing design guidelines and code examples rather than teaching the underlying programming languages.

Can I use matplotlib and seaborn to generate journal-ready plots?▼

Yes, you can use matplotlib and seaborn to generate journal-ready plots, as this Skill offers specific instructions to ensure your Python figures adhere to journal formatting and accessibility standards.

How do I ensure my ggplot2 figures meet journal formatting standards?▼

To ensure your ggplot2 figures meet journal formatting standards, this Skill provides specific guidelines covering DPI, vector versus raster outputs, color-blind safe palettes, and multi-panel layout configurations.

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