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scientific-visualization

Generate publication-ready scientific figures from matplotlib, seaborn, or plotly code.

4|Updated Mar 2, 2026
Search Tags:#matplotlib#publication figures#seaborn#scientific visualization#colorblind-safe palettes#error bars#journal export
One-click install
npx skills add https://github.com/shushuzn/Rairos --skill scientific-visualization-shushuzn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/shushuzn/Rairos/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/shushuzn/Rairos --skill scientific-visualization-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you turn scientific data into clear, publication-ready figures that meet common journal requirements for typography, layout, resolution, and accessibility (including colorblind-safe palettes).

Core Features & Use Cases

  • Journal-ready figure styling: Apply consistent, publication-focused defaults (fonts, line widths, spacing) and journal-specific presets for Nature/Science/Cell-style output.
  • Publication-compliant export: Save figures in correct formats (vector-first for plots, proper raster DPI for images) to produce submission-ready outputs (PDF/EPS/TIFF/PNG as needed).
  • Accessible color and layout: Use colorblind-friendly palettes (e.g., Okabe-Ito), support multi-panel layouts, and ensure grayscale interpretability.
  • Workflow for common figure types: Line plots with uncertainty, multi-panel composites, heatmaps with appropriate colormaps, and significance annotations with error bars.

Quick Start

Use the scientific-visualization skill to create a Nature-style multi-panel manuscript figure by applying the journal configuration, plotting your data with publication-safe colors and labels, then exporting in the required formats.

Frequently Asked Questions about scientific-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I export matplotlib figures as vector-friendly PDF or EPS files for journal submission?▼

Apply journal-specific figure styling in matplotlib using built-in presets for Nature, Science, and Cell-style output. The skill sets publication-focused defaults for fonts, line widths, and spacing to meet common journal requirements.

How do I create colorblind-safe plots in seaborn that remain readable in grayscale?▼

Create colorblind-safe plots in seaborn using accessible palettes like Okabe-Ito that ensure grayscale interpretability. The skill applies colorblind-friendly defaults and grayscale-compatible styling to make figures accessible without manual color tuning.

How do I add error bars and significance annotations to a multi-panel scientific figure?▼

Use this skill for publication-quality scientific figures from matplotlib, seaborn, and plotly code. It handles journal-specific figure preparation, multi-panel layouts, error bars, colorblind-safe palettes, and correct resolution exports for line art versus photographs.

What's the best way to set journal-compliant typography and sizing for manuscript figures?▼

Set journal-compliant typography and sizing for manuscript figures using preset style utilities that apply publication-focused defaults. The skill configures correct fonts, sizing, and spacing to satisfy journal-specific requirements for manuscript submission.

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