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scientific-visualization

Generate publication-ready scientific figures with Matplotlib and Seaborn.

1|Updated Mar 12, 2026
Search Tags:#visualization#matplotlib#seaborn#publication#data plotting#figure creation
One-click install
npx skills add https://github.com/yf8578/clawomics --skill scientific-visualization-yf8578
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/yf8578/clawomics/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/yf8578/clawomics --skill scientific-visualization-yf8578

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 figures for scientific manuscripts, ensuring clarity, accessibility, and adherence to journal standards.

Core Features & Use Cases

  • Publication-Ready Figures: Generate multi-panel layouts, add significance annotations, error bars, and colorblind-safe palettes.
  • Journal Formatting: Orchestrates matplotlib/seaborn/plotly with specific publication styles (Nature, Science, Cell).
  • Use Case: Prepare a set of figures for a journal submission, ensuring they meet all technical requirements for resolution, color, and layout.

Quick Start

Use the scientific-visualization skill to create a publication-quality line plot with error bars.

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 with Matplotlib and Seaborn for a journal submission?▼

Yes, this Skill applies specific publication styles for major journals like Nature, Science, and Cell. It configures your Matplotlib and Seaborn plots to meet the technical resolution, color, and layout requirements of these publications.

What's the best way to add significance annotations and error bars to scientific data plots?▼

The best way to add significance annotations and error bars is through this Skill's advanced figure creation capabilities. It handles these specific scientific plotting annotations automatically, ensuring clarity and accurate data visualization across multi-panel layouts.

Does this Skill support interactive Plotly figures alongside static Matplotlib layouts?▼

You need Python plotting libraries including Matplotlib, Seaborn, Plotly, NumPy, SciPy, and Pandas installed in your environment. These dependencies provide the foundational data manipulation and figure creation capabilities required to generate scientific plots.

Can I generate multi-panel layouts with colorblind-safe palettes using Python plotting libraries?▼

Yes, you can generate multi-panel layouts with colorblind-safe palettes using this Skill. It facilitates accessible scientific data visualization by configuring Matplotlib and Seaborn to apply colorblind-safe colors across complex figure layouts.

How do I format scientific figures to meet specific journal requirements for resolution and color?▼

Format scientific figures by applying this Skill's journal-specific formatting rules. It addresses the need for technical compliance by setting exact resolution, color, and layout parameters required for successful manuscript submission.

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