scientific-visualization

Name: scientific-visualization
Availability: InStock
Author: wsxwj123

Create multi-panel publication figures with colorblind-safe palettes and journal-compliant exports.

7|Updated Jan 27, 2026

Search Tags:#plotly #matplotlib #seaborn #publication #figures #colorblind #journal-exports

Ready to run

npx skills add https://github.com/wsxwj123/opencode-skills-backup --skill scientific-visualization-wsxwj123

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/wsxwj123/opencode-skills-backup/tree/main/scientific-visualization
Command: npx skills add https://github.com/wsxwj123/opencode-skills-backup --skill scientific-visualization-wsxwj123

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Publication and data-communication professionals often spend excessive effort crafting publication-ready figures that meet journal guidelines and accessibility standards. This skill provides a structured, reusable workflow to produce clean, accurate, and visually consistent figures across multiple panels and formats.

Core Features & Use Cases

Multi-panel figure orchestration with consistent styling compatible with Nature, Science, Cell, and PLOS guidelines.
Colorblind-friendly palettes and typography guidelines to ensure accessibility and readability.
Export utilities to generate vector (PDF/EPS/SVG) or high-DPI raster (TIFF/PNG) figures that conform to journal requirements.

Quick Start

Create publication-ready figures by applying journal styles, color-safe palettes, and export settings.

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 specific journal guidelines?▼

Publication-ready figures are generated by applying predefined styles for journals like Nature, Science, Cell, and PLOS, ensuring multi-panel layouts, specific dimensions, and compliant typography.

How do I export matplotlib figures to vector and high-DPI raster formats?▼

Export matplotlib figures to vector formats like PDF, EPS, and SVG, or high-DPI raster formats like TIFF and PNG, using built-in utilities that satisfy journal technical requirements.

Can I use colorblind-safe palettes for multi-panel scientific figures?▼

Yes, colorblind-safe palettes are integrated into the styling workflow to ensure multi-panel scientific figures maintain accessibility and readability without visual bias.

Does this scientific visualization workflow require matplotlib as a dependency?▼

Yes, matplotlib is required as the core dependency to render multi-panel layouts, apply accessible color guidelines, and export publication-grade figures.

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

Consistent typography across multi-panel scientific figures is achieved by applying reusable, journal-specific styling rules that enforce uniform font types and sizes throughout the layout.

Why do my journal figure exports fail technical requirements for dimensions and DPI?▼

Figure exports fail technical requirements when dimensions and DPI are not properly configured; this skill applies journal-specific dimensions and high-DPI export settings to ensure compliance.