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scientific-visualization

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

Updated Mar 30, 2026
Search Tags:#visualization#plotly#matplotlib#seaborn#publication#figures#colorblind
One-click install
npx skills add https://github.com/erlebach/gordon --skill scientific-visualization-erlebach
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/erlebach/gordon/tree/main/skills/illustration_skills/scientific-visualization
Command: npx skills add https://github.com/erlebach/gordon --skill scientific-visualization-erlebach

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Publication-quality figures often require careful styling, consistent typography, and format-specific outputs, which can be time-consuming to produce manually. This Skill provides a streamlined workflow to generate high-quality scientific figures from data using Matplotlib, Seaborn, and Plotly, ensuring they are ready for journal submission.

Core Features & Use Cases

  • Multi-panel figure layouts with consistent styling across panels
  • Support for error bars, significance markers, and statistical annotations
  • Colorblind-friendly palettes and accessible typography for print and grayscale
  • Export options to PDF, EPS, TIFF, and PNG; vector formats for publication quality
  • Library compatibility across Matplotlib, Seaborn, and Plotly, with utilities to check figure size and font embedding
  • Real-world use: create a journal-ready figure combining time-series, bar, and heatmap panels to illustrate experimental results

Quick Start

Configure the publication style and create a basic multi-panel figure using Matplotlib to begin.

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 with Matplotlib for journal submission?▼

Publication-ready figures are generated automatically using deterministic styling presets in Matplotlib, Seaborn, and Plotly to ensure consistent typography and format-specific outputs for journal submission. It supports multi-panel layouts, error bars, and significance markers.

Can I export Matplotlib figures to PDF, EPS, and TIFF formats for journals?▼

Yes, you can export figures to PDF, EPS, TIFF, and PNG formats. The Skill includes utilities to verify figure size, embed fonts, and export figures in journal-specific vector formats required for publication quality.

Does this visualization tool support colorblind-friendly palettes?▼

Yes, it supports colorblind-friendly palettes and accessible typography for print and grayscale. This ensures generated scientific figures are accessible and meet publication standards for visual clarity.

How do I build multi-panel layouts with error bars and significance markers?▼

Multi-panel figure layouts with consistent styling are built using Matplotlib, Seaborn, and Plotly. The Skill directly supports adding error bars, significance markers, and statistical annotations across panels to illustrate experimental results.

What's the best way to ensure consistent styling across multiple scientific figure panels?▼

The best way to ensure consistent styling is by applying deterministic styling presets. This Skill provides streamlined workflows and presets to maintain consistent typography and styling across all panels in a multi-panel scientific figure.

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