How does it handle complex multi-panel figures?

It utilizes Matplotlib's GridSpec and specialized helpers to align multiple plots into a single figure with consistent font sizes and bold panel labels (A, B, C).

Can I export figures in vector formats?

Absolutely. The skill supports high-quality vector formats like PDF, EPS, and SVG, which are preferred by journals for sharp line art and text.

Which journals are supported by this skill?

The skill includes specific style presets for major journals like Nature, Science, Cell, and PLOS, but it can be customized for any publication's specific guidelines.

Does this skill handle colorblind accessibility?

Yes, it prioritizes colorblind-safe palettes like Okabe-Ito and Viridis, and includes functionality to verify that figures remain interpretable in grayscale.

Scientific Visualization

Name: Scientific Visualization
Author: x-cmd

byx-cmd

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Ciencia de Datos y ML

Generates publication-ready scientific figures with journal-specific styling, colorblind-safe palettes, and high-resolution export formats.

This skill empowers researchers and data scientists to transform raw data into professional, manuscript-quality visualizations using Matplotlib, Seaborn, and Plotly. It streamlines the creation of complex multi-panel layouts with consistent labeling, ensures accessibility through colorblind-safe palettes, and automates compliance with strict journal requirements from publishers like Nature, Science, and Cell. By handling technical details like vector export, DPI settings, and statistical markers, it allows users to focus on data insights while ensuring the final output meets the highest academic standards.

Características Principales

01High-resolution export in vector (PDF, EPS) and raster (TIFF, PNG) formats

02Journal-specific style presets for Nature, Science, Cell, and PLOS

03Colorblind-accessible palettes and automated grayscale compatibility testing

048 GitHub stars

05Flexible multi-panel figure generation with automated bold labeling

06Built-in statistical markers for significance, error bars, and sample sizes

Casos de Uso

01Creating high-resolution, accessible plots for academic posters and presentations

02Standardizing data visualization styles across a research lab or project

03Preparing figures for submission to peer-reviewed scientific journals

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add x-cmd/skill scientific-visualization

For use in Claude.ai and ChatGPT

Download Skill