scientific-visualization

Automate publication-ready scientific figures with Matplotlib, Seaborn, and Plotly.

1|Updated Apr 4, 2026

Search Tags:#visualization #plotly #matplotlib #seaborn #figures #publication-quality #journal-guidelines

Ready to run

npx skills add https://github.com/m0at/claudemd --skill scientific-visualization-m0at

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/m0at/claudemd/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/m0at/claudemd --skill scientific-visualization-m0at

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This capability helps researchers produce publication-ready figures for journals by automating publication-style styling, multi-panel layouts, and accessibility considerations, reducing back-and-forth polishing.

Core Features & Use Cases

Multi-panel figure orchestration with consistent styling across panels for Nature, Science, Cell, and similar journals.
Colorblind-friendly palettes and typography guidance to ensure readability and accessibility.
Library-agnostic workflows that integrate with Matplotlib, Seaborn, and Plotly, plus preset journal configurations for quick, reproducible figures.
Use cases include preparing figures for manuscripts, grant reports, and supplementary materials where high-quality visuals are required.

Quick Start

Create a publication-ready figure by configuring the publication style and building a multi-panel figure using the included presets, then export at publication quality.

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 specific journal guidelines?▼

Publication-ready figures meeting journal guidelines are created by applying preset configurations for Nature, Science, and Cell using Matplotlib, Seaborn, and Plotly. This ensures adherence to typography, accessibility, and technical resolution standards for final manuscript submission.

Can I build multi-panel layouts with significance annotations using matplotlib?▼

Multi-panel layouts with significance annotations are fully supported using matplotlib. The workflow orchestrates consistent styling across all panels and applies necessary statistical annotations to produce cohesive, journal-quality scientific figures.

Does this workflow support exporting vector formats like PDF and SVG at 300 DPI?▼

Exporting vector formats like PDF, EPS, and SVG is supported alongside 300 to 1200 DPI raster options. The workflow embeds fonts and applies journal-specific technical formatting to satisfy strict publication resolution requirements.

How do I ensure my scientific plots use colorblind-friendly palettes?▼

Colorblind-friendly palettes are applied automatically through the styling presets. The workflow integrates accessibility guidelines and typography adjustments to ensure readability for colorblind readers across all generated scientific plots.

What is the best way to format multi-panel figures for a manuscript?▼

The best way to format multi-panel manuscript figures is using library-agnostic workflows that integrate Matplotlib, Seaborn, and Plotly. This approach applies consistent styling, significance annotations, and journal-specific configurations for quick, reproducible results.

Do I need matplotlib installed to generate publication-quality scientific figures?▼

Matplotlib is required as a dependency to generate publication-quality scientific figures. The workflow integrates this library alongside Seaborn and Plotly to apply multi-panel layouts, colorblind-friendly palettes, and journal-specific formatting.