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scientific-visualization

Generate publication-quality scientific figures with Matplotlib and Seaborn.

557|98|Updated Nov 7, 2025
Search Tags:#data visualization#visualization#matplotlib#seaborn#publication#figure creation
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill scientific-visualization-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/scientific-visualization
Command: npx skills add https://github.com/jimmc414/Kosmos --skill scientific-visualization-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, numpy, scipy, pandas, plotly, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the creation of publication-quality scientific figures, ensuring they meet journal standards for clarity, accessibility, and format.

Core Features & Use Cases

  • Journal-Ready Plots: Generate figures with correct sizing, resolution, and typography for top-tier journals.
  • Colorblind Accessibility: Utilizes colorblind-safe palettes and ensures figures are interpretable in grayscale.
  • Multi-Panel Figures: Facilitates the creation of complex figures with consistent styling across panels.
  • Use Case: You need to create a figure for a Nature submission showing time-course data with error bars and a bar chart comparing treatment groups, ensuring all elements adhere to Nature's strict formatting guidelines.

Quick Start

Use the scientific-visualization skill to create a publication-quality line plot with error bars and save it as a 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 journal formatting guidelines?▼

You create publication-ready figures by managing dimensions, resolution, typography, and color palettes with Matplotlib and Seaborn. This process ensures outputs meet strict journal formatting guidelines for clarity and accessibility.

Can I export scientific plots to vector formats like PDF and TIFF for journal submission?▼

Scientific plots can be exported to vector formats like PDF and EPS, alongside high-resolution raster formats like TIFF and PNG. This guarantees output files meet standard journal submission requirements.

How do I make a multi-panel figure with consistent styling across all panels?▼

Multi-panel figures are created by applying consistent styling, colorblind-safe palettes, and uniform typography across all subplots. This approach maintains visual coherence and accessibility throughout complex scientific figures.

Does this visualization approach support colorblind accessibility in scientific plots?▼

Colorblind accessibility is supported by utilizing colorblind-safe palettes and ensuring plots remain interpretable in grayscale. This guarantees scientific figures are accessible to a broader audience.

What Python libraries are needed to generate journal-compliant statistical plots?▼

Generating journal-compliant statistical plots requires Python libraries including Matplotlib, Seaborn, Plotly, NumPy, SciPy, and Pandas. These dependencies provide the computational and graphical foundation for figure creation.

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