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scientific-visualization

Create publication-quality multi-panel scientific figures with Matplotlib, Seaborn, and Plotly.

1|Updated Mar 4, 2026
Search Tags:#visualization#matplotlib#seaborn#publication#figures#colorblind#journal-standards
One-click install
npx skills add https://github.com/Hung-3008/agusta --skill scientific-visualization-hung-3008
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/scientific-visualization
Command: npx skills add https://github.com/Hung-3008/agusta --skill scientific-visualization-hung-3008

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, PyPDF2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Automates the creation of publication-quality figures for scientific manuscripts by coordinating plotting libraries (Matplotlib, Seaborn, and Plotly) with publication-style presets.

Core Features & Use Cases

  • Multi-panel figure layouts with consistent styling, panel labels, and layout alignment.
  • Colorblind-safe palettes and perceptually uniform colormaps for accessible visuals.
  • Journal-compliant exports (PDF/EPS/TIFF/PNG) with DPI and font-embedding guarantees, plus size validation.

Quick Start

Configure publication styles and generate a multi-panel figure suitable for journal submission.

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 journal standards for Nature or Science?▼

Publication-ready figures meeting journal standards are created by applying Matplotlib and Seaborn style presets for multi-panel layouts, colorblind-safe palettes, and compliant vector exports with correct DPI and font embedding.

What's the best way to ensure my scientific figures are colorblind-safe and readable in grayscale?▼

Colorblind-safe and grayscale-readable figures are ensured by applying perceptually uniform colormaps and accessible colorblind-safe palettes during the styling and export process for scientific publications.

How do I automate multi-panel figure layouts with consistent styling and panel labels in Matplotlib?▼

Multi-panel figure layouts with consistent styling and aligned panel labels are automated by coordinating Matplotlib, Seaborn, and Plotly using publication-style presets that enforce layout alignment and uniform formatting.

Does this Skill handle PDF and TIFF export with DPI targets and font embedding for scientific journals?▼

PDF, EPS, TIFF, and PNG exports with strict DPI targets and font-embedding guarantees are handled automatically, accompanied by size validation tooling to verify compliance with journal submission constraints.

Can I use Plotly alongside Matplotlib and Seaborn for generating compliant scientific figures?▼

Plotly is coordinated alongside Matplotlib and Seaborn to generate scientific figures, applying publication-style presets that enforce consistent styling, accurate labels, and compliant export formats across all three libraries.

Why do my exported figures fail journal size validation and how can I verify them?▼

Figures fail journal size validation when dimensions or file properties exceed submission limits; included tooling verifies size and journal constraints to ensure compliant PDF, EPS, TIFF, or PNG exports.

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