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scientific-visualization

Generate publication-ready multi-panel figures from data using Matplotlib, Seaborn, or Plotly.

15|2|Updated Dec 17, 2025
Search Tags:#visualization#plotly#matplotlib#seaborn#publication#figures#colorblind
One-click install
npx skills add https://github.com/rubensliv/k-dense-ai --skill scientific-visualization-rubensliv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/scientific-visualization
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill scientific-visualization-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Publication-quality figures are time-consuming to craft and must adhere to journal guidelines; this skill automates and standardizes the creation and export of publication-ready visuals from your data.

Core Features & Use Cases

  • Multi-panel figure layouts with consistent styling for manuscript figures.
  • Support for error bars, significance markers, and annotations.
  • Colorblind-friendly palettes and accessibility considerations.
  • Export in vector and raster formats (PDF, EPS, TIFF, PNG) with per-journal dimension presets.
  • Compatible with Matplotlib, Seaborn, and Plotly for static and interactive figures.

Quick Start

Create a publication-ready multi-panel figure from a dataset and export it as PDF and TIFF using the default color palette.

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 that meet journal dimension requirements?▼

Publication-ready figures are generated using per-journal dimension presets for publications like Nature, Science, and PLOS. The skill standardizes your data plots into compliant visuals, exporting them directly to PDF, EPS, TIFF, or PNG formats.

Can I use Matplotlib and Seaborn to build multi-panel scientific figures?▼

Yes, Matplotlib and Seaborn are fully supported for constructing multi-panel scientific figures. The skill applies consistent styling across all panels, incorporating error bars, significance markers, and annotations for your manuscript.

How do I make my scientific data plots accessible with colorblind-friendly palettes?▼

Colorblind-friendly palettes are automatically applied to your scientific data plots to ensure accessibility. This standardizes visual outputs so figures remain readable and safe for diverse audiences without manual color adjustments.

What is the best way to export Matplotlib figures to vector and raster formats for submission?▼

Exporting Matplotlib figures to vector and raster formats is handled automatically with embedded fonts. The skill outputs submission-ready PDF, EPS, TIFF, and PNG files, adhering to specific journal dimension presets.

Does this skill support interactive Plotly figures for scientific manuscripts?▼

Yes, Plotly is supported alongside Matplotlib and Seaborn for generating both static and interactive scientific figures. This allows you to visualize complex datasets like heatmaps and line plots before exporting to publication standards.

Do I need Matplotlib installed to generate scientific visualizations with this skill?▼

Yes, Matplotlib is a required dependency to generate these scientific visualizations. While Seaborn and Plotly are supported for specialized plotting, the underlying figure styling and export logic relies on the Matplotlib environment.

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