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scientific-visualization

Create publication-ready figures with Matplotlib, Seaborn, and Plotly using journal-specific styles.

Updated Apr 16, 2026
Search Tags:#visualization#publication#figures#colorblind-friendly#multi-panel#journal-formatting
One-click install
npx skills add https://github.com/ejoliet/claude-skills --skill scientific-visualization-ejoliet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/ejoliet/claude-skills/tree/main/scientific-visualization
Command: npx skills add https://github.com/ejoliet/claude-skills --skill scientific-visualization-ejoliet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creating publication-ready figures that comply with journal guidelines and typography is time-consuming and error-prone; this skill orchestrates Matplotlib, Seaborn, and Plotly with publication styles to ensure consistent, accessible visuals across manuscripts.

Core Features & Use Cases

  • Multi-panel figure layouts with consistent styling for journals like Nature, Science, and Cell.
  • Colorblind-safe palettes, typography guidelines, and vector-exportable outputs (PDF/EPS/TIFF).
  • Templates and quick-start examples for common figure types (line plots, heatmaps, boxplots) and journal-specific formatting.

Quick Start

Configure publication styles for your target journal, assemble a multi-panel figure, and export in publication-quality formats.

Frequently Asked Questions about scientific-visualization

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

FAQPage Schema
How do I create publication-ready figures that meet Nature, Science, or Cell journal formatting guidelines?▼

Publication-ready figures for Nature, Science, and Cell are created by applying configurable journal-specific templates to ensure consistent typography and styling. The skill enforces these guidelines across multi-panel layouts using Matplotlib, Seaborn, and Plotly.

How do I generate colorblind-friendly multi-panel layouts in Matplotlib for a manuscript?▼

Colorblind-friendly multi-panel layouts are generated using built-in accessible palettes and consistent typography templates. The skill orchestrates Matplotlib, Seaborn, and Plotly to assemble aligned panels that comply with manuscript requirements.

Can I export vector graphics in PDF, EPS, or TIFF formats for journal submission?▼

Yes, vector exports in PDF, EPS, and TIFF formats are fully supported for journal submission. The skill ensures high-quality publication outputs by enforcing journal-specific styling and typography guidelines before export.

Does this skill provide templates for common scientific plot types like heatmaps and boxplots?▼

Yes, quick-start templates for common scientific plot types including line plots, heatmaps, and boxplots are provided. These templates apply journal-specific formatting and colorblind-safe palettes to ensure accessible manuscript visuals.

What is the best way to ensure typography consistency across multi-panel figures for different journals?▼

Typography consistency across multi-panel figures is ensured by applying configurable journal-specific style templates. This approach maintains uniform font sizes and styles across all panels, supporting manuscripts for Nature, Science, and Cell.

Can I use Plotly with Matplotlib to create journal-formatted multi-panel figures?▼

Yes, Plotly works alongside Matplotlib and Seaborn to create journal-formatted multi-panel figures. The skill orchestrates these libraries to produce consistent, accessible visuals that meet specific journal guidelines and export requirements.

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