scientific-visualization

Create publication-ready multi-panel figures from data using matplotlib, seaborn, or plotly.

1|Updated Apr 12, 2026

Search Tags:#visualization #plotly #matplotlib #seaborn #publication #figures #colorblind

Ready to run

npx skills add https://github.com/wrayre/Agentic_Coding_Workshop --skill scientific-visualization-wrayre

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/wrayre/Agentic_Coding_Workshop/tree/main/.github/skills/scientific-visualization
Command: npx skills add https://github.com/wrayre/Agentic_Coding_Workshop --skill scientific-visualization-wrayre

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, PyPDF2, and includes scripts (resource) components.

What problem does it solve?

Converts raw data into publication-ready figures, handling layout, annotations, color accessibility, and journal formatting to save time and improve quality.

Core Features & Use Cases

Multi-panel figure orchestration with consistent styling for journal submissions (Nature, Science, Cell, PLOS, etc.)
Colorblind-friendly palettes and typography guidelines to ensure accessibility
Export-ready outputs across vector and raster formats (PDF, EPS, SVG, TIFF, PNG) compatible with major publishers
Works across plotting libraries (matplotlib, seaborn, plotly) with publication-style presets
Use case: generate a submission-ready figure set from a dataset including a main panel, supplemental panels, and annotated significance markers.

Quick Start

Generate a publication-ready figure from your dataset using the Nature-style preset.

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 for journal submission using matplotlib?▼

Publication-ready figures for journals like Nature and Science are created by applying consistent styling, multi-panel layouts, and error bars. This process handles annotations and journal formatting to produce submission-ready outputs.

What's the best way to export matplotlib and seaborn figures to vector formats for publishers?▼

Exporting figures to vector and raster formats for publishers is supported across PDF, EPS, SVG, and TIFF. The process ensures outputs meet major publisher specifications directly from the plotting libraries.

Can I generate colorblind-friendly palettes for scientific figures?▼

Colorblind-friendly palettes for scientific figures are generated using built-in accessibility considerations. Typography guidelines and specific color palettes ensure figures remain accessible without manual adjustments.

Does this approach work with plotly as well as matplotlib for multi-panel layouts?▼

Plotly is supported alongside matplotlib and seaborn for multi-panel layouts. The orchestration applies publication-style presets across these plotting libraries to maintain consistent styling.

How do I add annotated significance markers to a main figure panel?▼

Annotated significance markers are added to a main panel during the figure generation process. The orchestration creates a submission-ready figure set including main panels, supplemental panels, and markers.

When do I need PyPDF2 for scientific visualization?▼

PyPDF2 is required as a dependency for scientific visualization workflows involving PDF outputs. It enables processing and manipulation of exported PDF figures generated for journal submissions.