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scientific-visualization

Generate publication-ready multi-panel figures with journal-specific styles and export utilities.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#visualization#plotly#matplotlib#seaborn#colorblind#publication-quality#mult-panel
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill scientific-visualization-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/scientific-visualization
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill scientific-visualization-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Publication-quality figures are time-consuming to produce and often fail journal-specific guidelines, color accessibility standards, and consistent styling across panels. This Skill provides a cohesive framework to generate publication-ready figures by coordinating publication styles, color palettes, multi-panel layouts, and export utilities to streamline journal submissions.

Core Features & Use Cases

  • Orchestrates multi-panel figures with consistent styling across panels for manuscripts.
  • Enforces colorblind-safe palettes and typography to ensure accessibility in print and grayscale.
  • Supplies publication-ready exports in vector (PDF/EPS/SVG) and high-resolution raster (TIFF/PNG) formats with appropriate DPI settings.
  • Applicable to biology, chemistry, and data-heavy disciplines requiring publication-grade visualizations and journal-compliant figure layouts.

Quick Start

Configure a journal style, apply a color palette, assemble a two-by-two panel figure, and export to PDF and PNG.

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 Nature or Science journal guidelines?▼

Publication-ready figures meeting Nature, Science, Cell, and PLOS guidelines are generated by applying preset styles, colorblind-safe palettes, and high-resolution export utilities. This framework enforces journal-specific font, size, DPI, and accessibility requirements directly.

Can I assemble multi-panel matplotlib figures with consistent styling for a manuscript?▼

Yes, multi-panel figures can be assembled with consistent styling across all panels for manuscripts. The framework coordinates typography and layout across subplots to ensure visual consistency throughout the entire figure.

How do I export plots to vector and high-resolution raster formats for journal submission?▼

Plots can be exported to vector formats like PDF, EPS, and SVG, or high-resolution raster formats like TIFF and PNG. The export utilities automatically apply appropriate DPI settings required for journal submissions.

Does this visualization tool provide colorblind-safe palettes for grayscale print compatibility?▼

Yes, colorblind-safe palettes and typography are enforced to ensure accessibility in both print and grayscale. This guarantees that figures remain legible and compliant with accessibility standards across different viewing mediums.

Are preset publication styles available for generating heatmaps and time-series plots?▼

Preset publication styles are available for generating heatmaps, box plots, and time-series plots. These styles satisfy strict font, size, and color requirements, streamlining the creation of journal-compliant visualizations.

What is the best way to ensure consistent typography across multi-panel scientific visualizations?▼

Consistent typography across multi-panel scientific visualizations is ensured by applying coordinated publication styles. This framework manages font and size requirements uniformly across all panels during figure assembly and export.

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