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scientific-visualization

Generate publication-quality scientific figures with matplotlib, seaborn, and plotly.

Updated Feb 26, 2026
Search Tags:#data visualization#journal submission#publication#scientific visualization#figure creation
One-click install
npx skills add https://github.com/gracefullight/iqc --skill scientific-visualization-gracefullight
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/gracefullight/iqc/tree/main/.agents/skills/scientific-visualization
Command: npx skills add https://github.com/gracefullight/iqc --skill scientific-visualization-gracefullight

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the creation of publication-ready scientific figures, ensuring clarity, accuracy, and accessibility.

Core Features & Use Cases

  • Publication Styles: Apply predefined journal-specific styles.
  • Data Visualization: Generate high-quality line plots, bar plots, heatmaps, and more.
  • Customization: Fine-tune figure appearance, from font sizes to color schemes.
  • Use Case: If you need to create a line plot for a journal submission, this Skill can automatically apply the appropriate style and save the figure in the correct format.

Quick Start

Create a line plot with error bars and save it as a PDF for publication using the following code snippet:

import matplotlib.pyplot as plt
import numpy as np
from style_presets import apply_publication_style
apply_publication_style('default')
fig, ax = plt.subplots(figsize=(3.5, 2.5))
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.errorbar(x, np.sin(x), yerr=np.sin(x) * 0.1, label='error')
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Amplitude (mV)')
ax.legend()
fig.savefig('line_plot.pdf')

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 scientific figures for journal submission?▼

You can create publication-ready scientific figures by applying predefined publication styles using matplotlib, seaborn, and plotly. This Skill orchestrates these libraries to automatically apply appropriate journal-specific formatting and save figures in the correct format.

Can I generate colorblind-safe palettes and multi-panel layouts for scientific visualization?▼

Yes, generating colorblind-safe palettes and multi-panel layouts is supported for scientific visualization. The Skill applies predefined publication styles to ensure accessibility and accurate formatting required for journal submissions.

Do I need matplotlib, seaborn, and plotly installed to use this scientific visualization Skill?▼

Yes, you need matplotlib, seaborn, and plotly installed to use this scientific visualization Skill. These libraries are required dependencies for generating the high-quality line plots, bar plots, and heatmaps.

What's the best way to apply journal-specific styles to a line plot with error bars?▼

The best way to apply journal-specific styles to a line plot with error bars is to use the predefined publication style presets. You can apply a style and then save the customized figure as a PDF for publication.

How does this approach compare to manually customizing matplotlib for data visualization?▼

Compared to manually customizing matplotlib for data visualization, this approach streamlines the process by automatically orchestrating styles, colorblind-safe palettes, and specific journal formatting. This ensures clarity and accessibility without manual fine-tuning.

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