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scientific-visualization

Create publication-ready figures with journal-style formatting and colorblind-safe palettes.

Updated Jun 7, 2026
Search Tags:#visualization#matplotlib#publication#figures#colorblind#journal-guidelines
One-click install
npx skills add https://github.com/schneidermu/agent-dotfiles --skill scientific-visualization-schneidermu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/schneidermu/agent-dotfiles/tree/main/codex-skills/scientific-visualization
Command: npx skills add https://github.com/schneidermu/agent-dotfiles --skill scientific-visualization-schneidermu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The skill helps researchers produce publication-ready figures by providing a cohesive workflow for styling, multi-panel layouts, and compliance with journal guidelines.

Core Features & Use Cases

  • Integrated publication styles: pre-configured journal presets to enforce typography, sizes, and color usage, enabling real-world tasks like turning experimental results into Nature-ready figures.
  • Colorblind-safe palettes: built-in palettes and utilities to ensure accessibility in figures.
  • Multi-panel figure assembly & export: convenient creation of complex figures with consistent styling and automated export in vector and raster formats for submission.

Quick Start

Provide your dataset to the workflow and generate publication-ready figures using the included styles.

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 from data using matplotlib?▼

You create publication-ready figures by applying pre-configured journal style presets to matplotlib, enforcing consistent typography, sizing, and accessible color palettes for final manuscript submission.

Can I generate colorblind-safe plots for journal submission?▼

Yes, you can generate colorblind-safe plots using built-in accessible color palettes and utilities, ensuring your journal submission figures remain readable to reviewers with visual impairments.

Does this workflow support multi-panel figure assembly and export?▼

Yes, the workflow supports multi-panel figure assembly with consistent styling across all subplots, and it automates export in both vector and raster formats for journal submission.

What's the best way to format matplotlib figures to meet specific journal guidelines?▼

The best way to meet journal guidelines is applying integrated publication style presets that automatically configure typography, dimensions, and color usage to match target journal requirements.

How do I test if my matplotlib figures remain legible in grayscale?▼

You test figure grayscale legibility using the built-in grayscale testing utility, which simulates black-and-white printing to verify that visual distinctions remain clear without color.

Do I need matplotlib installed to use this publication figure workflow?▼

Yes, you need matplotlib installed as the core dependency, because the workflow directly applies its publication styles, colorblind-safe palettes, and multi-panel layout configurations.

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