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scientific-visualization

Generate publication-ready matplotlib/seaborn figures with journal-specific styles and export settings.

Updated May 24, 2026
Search Tags:#matplotlib#scientific figures#journal formatting#multi-panel layout#colorblind accessibility#figure export#error bars
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill scientific-visualization-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/scientific-visualization
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill scientific-visualization-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Scientific authors need figures that are accurate, consistent, and compliant with journal submission requirements, including typography, resolution, multi-panel layouts, and colorblind-safe visual design.

Core Features & Use Cases

  • Journal-grade styling: Apply consistent matplotlib presets for outlets like Nature/Science/Cell-style formatting (fonts, sizes, line widths, layout defaults).
  • Accessible color and colormaps: Use colorblind-friendly categorical palettes (e.g., Okabe–Ito) and perceptually uniform sequential/diverging colormaps, with grayscale compatibility guidance.
  • Correct export workflows: Save in publication-appropriate formats (vector-first when possible) and resolution/DPI settings via export utilities, including journal-specific configurations.
  • Figure construction patterns: Support common scientific figure types—line plots with error bars, multi-panel layouts, heatmaps with labeled colorbars, and significance annotations—using best-practice guidance.

Quick Start

Generate a journal-compliant multi-panel figure from your prepared dataset by calling apply_publication_style for your target journal style, then save the final figure with save_for_journal to ensure the correct formats and DPI.

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?▼

Create publication-ready scientific figures by applying consistent journal-oriented matplotlib styles, colorblind-safe palettes, and correct export settings. The workflow covers line plots, multi-panel layouts, heatmaps, and statistical annotations for manuscript submission.

How do I format multi-panel layouts for journal submission in Python?▼

Format multi-panel layouts by applying journal-grade styling presets that configure fonts, sizes, and line widths for specific journals. The styling utilities ensure proper layout defaults and correct typography for typical submission workflows.

What is the best way to make colorblind-accessible scientific plots?▼

Make colorblind-accessible scientific plots using colorblind-friendly categorical palettes like Okabe–Ito and perceptually uniform sequential or diverging colormaps. The styling utilities also provide grayscale compatibility guidance for accessible visual design.

Does this support exporting matplotlib figures in vector formats for journals?▼

Exporting matplotlib figures in vector formats is supported through export utilities that save in publication-appropriate formats with vector-first prioritization. The export settings apply journal-specific resolutions and DPI configurations.

Can I add error bars and significance annotations to scientific figures?▼

You can add error bars and significance annotations using the provided figure construction patterns. The utilities support common scientific figure types including line plots with error bars, labeled heatmap colorbars, and statistical annotations.

Why do my matplotlib figures fail journal submission formatting checks?▼

Matplotlib figures fail journal formatting checks due to incorrect typography, insufficient resolution, or non-compliant format constraints. The styling utilities apply journal-specific saving requirements to ensure correct formats and DPI settings.

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