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scientific-visualization

Generate journal-ready scientific figures from matplotlib plotting code.

Updated May 26, 2026
Search Tags:#journal submission#figure export#error bars#publication-ready plots#matplotlib styling#colorblind palettes
One-click install
npx skills add https://github.com/XWIlluDelu/agent-share --skill scientific-visualization-xwilludelu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/XWIlluDelu/agent-share/tree/main/lib/scientific-visualization
Command: npx skills add https://github.com/XWIlluDelu/agent-share --skill scientific-visualization-xwilludelu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Scientific visualization turns raw data into clear, accurate, submission-ready figures that meet common journal formatting, accessibility, and statistical presentation requirements.

Core Features & Use Cases

  • Publication styling across journals: Apply consistent matplotlib settings (fonts, line widths, tick styles) tuned for common publishers.
  • Figure export that matches requirements: Save in appropriate vector and raster formats with correct DPI and size checks to reduce resubmission risk.
  • Accessibility-first color and readability: Use colorblind-safe palettes (e.g., Okabe–Ito) and design for grayscale interpretability.
  • Common figure patterns for manuscripts: Support multi-panel layouts, error bars, significance markers, heatmaps, and statistically rigorous plot composition.

Quick Start

Configure the workflow for a target journal by applying the journal preset and then save your figure using the publication export utilities.

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 plots for journal submission using matplotlib?▼

To create publication-ready plots for journal submission, apply consistent matplotlib styling presets for fonts, line widths, and tick styles, then use figure export utilities to save outputs in vector or raster formats with correct DPI and sizing.

What's the best way to add error bars and significance markers to scientific figures?▼

Adding error bars and significance markers to scientific figures is handled by applying the skill's statistical graphics composition utilities, which support rigorous plot construction for multi-panel layouts and heatmaps.

Does this approach work with seaborn and plotly for styling multi-panel layouts?▼

Yes, seaborn and plotly are supported dependencies for styling multi-panel layouts, allowing you to apply journal-appropriate typography and sizing alongside colorblind-safe palettes like Okabe-Ito.

How do I export matplotlib figures to PDF or TIFF with correct DPI for journals?▼

Exporting matplotlib figures to PDF, EPS, TIFF, or PNG with correct DPI requires using the publication export utilities, which perform size checks and apply target journal settings to reduce resubmission risk.

Can I use colorblind palettes like Okabe-Ito for accessible scientific visualization?▼

Using colorblind palettes like Okabe-Ito for accessible scientific visualization is a core feature, ensuring your plots maintain readability and grayscale interpretability for publication.

What are the limitations of using matplotlib presets for journal-specific figure validation?▼

Matplotlib presets provide general journal-appropriate styling, but optional journal-specific validation checks are recommended before producing final outputs to ensure strict compliance with publisher formatting requirements.

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