What journals are supported by this skill?

The skill includes specific presets and standards for major journals including Nature, Science, PNAS, and Cell, covering specific column widths, font sizes, and layout requirements.

How are multi-panel figures composed?

It uses a programmatic approach with react-pdf for precise control over element positioning and gutters, allowing for complex arrangements that are exported as pixel-perfect vector PDFs.

Does it handle data plotting directly?

Yes, it can generate publication-quality plots using Python (Matplotlib, Seaborn) or R (ggplot2) with pre-configured settings for consistent line weights, tick marks, and fonts.

Can I create custom scientific icons?

The skill includes a dedicated icon generation script that uses a template-based approach to create minimalist, professional icons in the specific aesthetic style used by top journals.

How do I verify the figure looks correct before finishing?

The pipeline includes a mandatory visual QA step that renders the PDF to a PNG preview, which Claude then inspects to ensure all labels, alignments, and fonts meet publication standards.

Scientific Figures for Research

Name: Scientific Figures for Research
Author: neuromechanist

byneuromechanist

•

Data Science & ML

Creates publication-quality multi-panel scientific figures and diagrams for top-tier academic journals using a complete generation and composition pipeline.

The Scientific Figures skill empowers researchers to generate professional, peer-review-ready figures directly within Claude Code. It automates the entire production pipeline—from generating minimalist scientific icons and publication-standard data plots via Matplotlib or Seaborn to composing complex multi-panel layouts using react-pdf. By enforcing journal-specific standards for fonts, line weights, and colorblind-safe palettes, it ensures that every figure meets the rigorous requirements of journals like Nature, Science, and Cell, while including a built-in visual QA loop for pixel-perfect results.

Key Features

01Publication-quality data plotting with Matplotlib, Seaborn, and ggplot2 templates

02Integrated visual QA loop with PDF-to-PNG conversion and automated inspection

03Generation of minimalist, journal-style scientific icons and schematics

0422 GitHub stars

05Automated multi-panel figure composition using react-pdf for precise layout control

06Strict adherence to top-tier journal standards for dimensions, fonts, and line weights

Use Cases

01Standardizing plot styles and colorblind-safe palettes across an entire research paper

02Creating a 2x2 multi-panel figure with icons and data plots for a Nature or Science submission

03Designing high-resolution graphical abstracts and schematic diagrams for grant proposals

Key Features

01Publication-quality data plotting with Matplotlib, Seaborn, and ggplot2 templates

02Integrated visual QA loop with PDF-to-PNG conversion and automated inspection

03Generation of minimalist, journal-style scientific icons and schematics

0422 GitHub stars

05Automated multi-panel figure composition using react-pdf for precise layout control

06Strict adherence to top-tier journal standards for dimensions, fonts, and line weights

Use Cases

01Standardizing plot styles and colorblind-safe palettes across an entire research paper

02Creating a 2x2 multi-panel figure with icons and data plots for a Nature or Science submission

03Designing high-resolution graphical abstracts and schematic diagrams for grant proposals