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scientific-visualization

Generate publication-ready multi-panel figures using Matplotlib, Seaborn, and Plotly with PDF/PNG/SVG export.

1|Updated Jan 26, 2026
Search Tags:#visualization#publication#colorblind#Plotly#Matplotlib#Seaborn
One-click install
npx skills add https://github.com/Victory-Hugo/S2-Agent-Skill --skill scientific-visualization-victory-hugo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/Victory-Hugo/S2-Agent-Skill/tree/main/skills/writing/scientific-visualization
Command: npx skills add https://github.com/Victory-Hugo/S2-Agent-Skill --skill scientific-visualization-victory-hugo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Scientific visualization often requires coordinating multiple libraries and adhering to journal guidelines to produce publication-ready figures. This Skill provides an integrated approach to generate high-quality visuals by orchestrating Matplotlib, Seaborn, and Plotly with publication-grade styles and palettes.

Core Features & Use Cases

  • Multi-library orchestration: Seamlessly produce figures using Matplotlib, Seaborn, and Plotly with consistent publication styling.
  • Journal-ready outputs: Enforce size, resolution, color palettes, font embedding, and export formats aligned with major publishers.
  • Examples: Create multi-panel figures, heatmaps, violin plots, regression plots, and time-series with shaded error.

Quick Start

  • Configure publication style with: from scripts.style_presets import apply_publication_style; apply_publication_style('default').
  • Generate figure from dataset df using Matplotlib/Seaborn/Plotly to produce a publication-ready multi-panel figure with a colorblind-friendly palette.
  • Export: use the figure_export utilities to save as PDF and PNG with proper DPI and embedded fonts.

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 and Seaborn?▼

Publication-ready scientific figures require consistent styling across Matplotlib, Seaborn, and Plotly. You apply a publication style preset to enforce size, resolution, and colorblind-friendly palettes, then export multi-panel figures to PDF, PNG, or SVG with embedded fonts.

How do I generate colorblind-friendly multi-panel figures for a journal submission?▼

Colorblind-friendly multi-panel figures are generated by applying a default publication style preset to your data. This orchestrates Matplotlib, Seaborn, and Plotly to enforce color palettes, journal-specific sizes, and export formats aligned with major publishers.

Do I need Python and Matplotlib to export figures with embedded fonts?▼

Yes, you need Python with Matplotlib, Seaborn, and Plotly installed to export figures with embedded fonts. The Skill orchestrates these libraries to configure publication-grade styles and export outputs in PDF, PNG, and SVG formats with proper DPI.

Can I use Plotly to make journal-ready regression plots and heatmaps?▼

Yes, you can use Plotly alongside Matplotlib and Seaborn to make journal-ready regression plots and heatmaps. The Skill orchestrates these libraries to apply consistent publication styling, including colorblind-friendly palettes and journal-specific formats.

What's the best way to enforce journal-specific resolution and sizes for scientific plots?▼

The best way to enforce journal-specific resolution and sizes is applying a publication style preset to your dataset. This configures Matplotlib, Seaborn, and Plotly to enforce DPI, font embedding, and export formats aligned with major publishers.

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