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scientific-plotting

Automate publication-quality scientific figure creation from data using Python and R tools.

1|1|Updated Jan 23, 2026
Search Tags:#visualization#plotly#plotting#matplotlib#publication-quality#scientific-plotting
One-click install
npx skills add https://github.com/Hollis36/claude-skill --skill scientific-plotting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-plotting
Source: https://github.com/Hollis36/claude-skill/tree/main/scientific-plotting
Command: npx skills add https://github.com/Hollis36/claude-skill --skill scientific-plotting

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

科研绘图助手为研究者和科学家提供一站式绘制高质量学术图表的解决方案，减少从数据到图表的手工与格式化工作。

Core Features & Use Cases

  • 支持统计图、科学示意图、热力图、网络图等多种图表类型，并可与 SciencePlots、Plotly、Plotnine 等工具协同工作。
  • 适用于撰写论文、准备演示、数据分析报告等场景，能够快速输出符合期刊风格的可发表图表。

Quick Start

将数据输入到绘图工作流以生成可发表的图表。

Frequently Asked Questions about scientific-plotting

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

FAQPage Schema
How do I create publication-quality scientific figures from raw data?▼

Publication-quality scientific figures are generated by automating chart creation from your data using matplotlib and seaborn. The workflow applies journal styling, color palettes, and proper spacing to produce charts like histograms and heatmaps ready for publication.

Can I use matplotlib with scienceplots to style charts for journal submission?▼

Matplotlib works with scienceplots to apply journal-specific styling to your scientific plots. This combination ensures your figures meet publication requirements for fonts, spacing, and export formats.

What chart types are supported for scientific plotting in Python and R?▼

Scientific plotting supports histograms, heatmaps, networks, radar charts, Sankey diagrams, and facet grids. These chart types are compatible with matplotlib, seaborn, plotly, plotnine, and ggplot2 for comprehensive visualization coverage.

Do I need numpy installed to generate scientific plots?▼

Numpy is required as a dependency to generate scientific plots, supporting data manipulation and numerical operations. Matplotlib is also required to render the visualizations and apply publication-quality styling.

What is the best way to automate multi-tool compatible plotting across Python and R?▼

Automating multi-tool compatible plotting is achieved by using a unified workflow that supports matplotlib, seaborn, scienceplots, plotly, plotnine, and ggplot2. This approach generates consistent, publication-ready scientific figures across different visualization libraries.

Why does my scientific plot need specific color palettes and font settings?▼

Scientific plots need specific color palettes and font settings to satisfy journal styling requirements for publication. Applying these formatting standards ensures your charts meet the visual criteria expected by academic publications.

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