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scientific-visualization

Generate publication-ready scientific figures with Matplotlib and Seaborn.

8|Updated Jan 13, 2026
Search Tags:#data visualization#charts#visualization#plotting#matplotlib#seaborn#publication
One-click install
npx skills add https://github.com/hxk622/TokenDance --skill scientific-visualization-hxk622
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/visualization/scientific-visualization
Command: npx skills add https://github.com/hxk622/TokenDance --skill scientific-visualization-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, pandas, numpy, scipy, plotly, 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 they meet journal standards for clarity, accessibility, and format.

Core Features & Use Cases

  • Publication-Quality Plots: Generates figures with correct resolution, colorblind-safe palettes, and appropriate typography for journals like Nature, Science, and Cell.
  • Multi-Panel Figures: Orchestrates complex layouts with consistent styling across panels.
  • Journal Formatting: Automates adherence to specific journal requirements for dimensions, file types, and DPI.
  • Use Case: Prepare a multi-panel figure for a Nature submission, including line plots, scatter plots, and heatmaps, ensuring all elements meet 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 scientific figures that meet journal formatting requirements?▼

Publication-ready scientific figures are generated using Python libraries like Matplotlib and Seaborn, applying style presets and helper scripts to ensure correct resolution, file types, and specific journal dimensions.

Can I use Matplotlib and Seaborn to build multi-panel layouts for Nature or Science submissions?▼

Yes, Matplotlib and Seaborn support multi-panel layouts with consistent styling across panels. The Skill orchestrates complex figures to meet strict formatting guidelines for journals like Nature, Science, and Cell.

Does this visualization approach support colorblind-safe palettes for academic charts?▼

Colorblind-safe palettes are supported for academic charts, ensuring visual accessibility and clarity. Seaborn and Matplotlib style presets are utilized to apply these palettes consistently across all figure elements.

What is the best way to export plots as high-resolution PDF and PNG files for journal submission?▼

Exporting plots as high-resolution PDF and PNG files is handled by helper scripts for figure export. These scripts automate adherence to specific journal requirements for dimensions, DPI, and file types.

Can I use pandas and numpy data structures to plot scientific charts directly?▼

Yes, pandas and numpy data structures are fully supported dependencies for plotting scientific charts. Data from pandas DataFrames and numpy arrays can be directly visualized using Matplotlib and Seaborn.

Does Plotly work with this workflow for creating interactive scientific visualizations?▼

Plotly is included as a supported dependency, enabling the creation of interactive scientific visualizations. It complements Matplotlib and Seaborn, which primarily handle static, publication-ready outputs.

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