scientific-visualization

Create publication-ready scientific figures with multi-panel layouts and journal-specific exports.

2|Updated Apr 12, 2026

Search Tags:#plotly #matplotlib #seaborn #colorblind #journal-submission #publication-figures #figure-export

Ready to run

npx skills add https://github.com/Clay-HHK/claude-config --skill scientific-visualization-clay-hhk

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/scientific-visualization
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill scientific-visualization-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, PyPDF2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill helps researchers and authors produce clear, accurate, and publication-ready figures from raw data by removing manual styling, sizing, and export steps that often delay manuscript submission.

Core Features & Use Cases

Multi-panel layouts: GridSpec-based composition, consistent panel labeling, and alignment tools for complex figures.
Accessible color & styling: Colorblind-safe palettes, perceptually uniform colormaps, and typography presets tuned for journals.
Accurate exports & checks: DPI control, vector and raster export options, size compliance checks, and journal-specific presets for Nature/Science/Cell/PLOS.
Workflow examples: Prepare line plots with error bars, multi-panel combination figures, heatmaps with proper colormap choices, and export in PDF/EPS/TIFF for submission.

Quick Start

Create a multi-panel, Nature-style figure from a pandas DataFrame, apply the publication style, verify size and DPI, and export the figure as PDF and TIFF.

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 journal-specific sizing for Nature or Science?▼

Publication-ready scientific figures for Nature or Science are created by applying journal-specific sizing presets, multi-panel GridSpec layouts, and colorblind-safe palettes to your data, ensuring dimensions and DPI meet submission requirements.

Can I export matplotlib figures as vector PDF and raster TIFF for manuscript submission?▼

Yes, you can export matplotlib figures as vector PDF and raster TIFF formats, with automated DPI control, font embedding checks, and figure dimension validation to satisfy technical journal submission guidelines.

What is the best way to add error bars and significance markers to multi-panel plots?▼

The best way to add error bars and significance markers to multi-panel plots is by using GridSpec-based composition tools that automate consistent panel labeling, alignment, and accessible styling for complex scientific figures.

Does this scientific visualization tool support colorblind-safe palettes and perceptually uniform colormaps?▼

Yes, it supports colorblind-safe palettes and perceptually uniform colormaps, applying accessible color and typography presets tuned for journals to ensure clear and accurate data visualization.

How do I validate figure dimensions and DPI before submitting a manuscript?▼

You validate figure dimensions and DPI before manuscript submission by running automated size compliance checks against journal-specific presets for Nature, Science, Cell, and PLOS, verifying technical requirements are met.

Do I need numpy and PyPDF2 to prepare multi-panel publication figures?▼

Yes, numpy and PyPDF2 are required dependencies, along with matplotlib, to handle data arrays and process PDF exports when preparing multi-panel publication figures with proper layout and vector output.