scientific-visualization

Generate publication-ready scientific figures with multi-panel layouts and journal-specific styling.

345|12|Updated Apr 14, 2026

Search Tags:#visualization #plotly #matplotlib #seaborn #publication #figures #colorblind

Ready to run

npx skills add https://github.com/minicoohei/ai-agent-camp --skill scientific-visualization-minicoohei

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/scientific-visualization
Command: npx skills add https://github.com/minicoohei/ai-agent-camp --skill scientific-visualization-minicoohei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Automatically generate publication-quality scientific figures from data and analyses, reducing manual formatting and ensuring accessibility and journal compliance.

Core Features & Use Cases

Publication-ready figures with multi-panel layouts, error bars, significance markers, and colorblind-safe palettes.
Journal-specific styling with preconfigured palettes for Nature, Science, Cell, PLOS, ACS, and other venues.
High-resolution export support (PDF/EPS/TIFF) across Matplotlib/Seaborn/Plotly, with typography and labeling best practices.

Quick Start

Configure the publication style, create a figure with the correct size, plot data with labeled axes and units, and export using the provided figure_export utilities.

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 scientific figures with matplotlib?▼

To create publication-ready scientific figures with matplotlib, you configure the publication style, plot data with labeled axes and units, and export using figure utilities. This process automatically applies colorblind-safe palettes and journal-specific styling.

Can I use seaborn and plotly to export high-resolution PDFs for journals?▼

Yes, you can export high-resolution PDFs, EPS, and TIFFs across Matplotlib, Seaborn, and Plotly. The process applies typography and labeling best practices to ensure your figures meet journal export requirements.

Does this approach support multi-panel layouts with colorblind-safe palettes?▼

Yes, generating publication-quality figures includes support for multi-panel layouts, error bars, significance markers, and colorblind-safe palettes. These features are automatically applied to ensure accessibility and visual clarity.

What is the best way to apply journal-specific styling for Nature or Science?▼

The best way to apply journal-specific styling for Nature or Science is using preconfigured palettes tailored for major publishers. This ensures your figures meet exact submission guidelines without manual formatting.

Do I need matplotlib installed to generate these scientific figures?▼

Yes, matplotlib is a required dependency for generating these scientific figures. The styling and export utilities rely on it to handle multi-panel layouts and high-resolution outputs.