PRS Figure Creator

Generate CMYK-ready TIFF figures compliant with PRS journal requirements.

Updated Nov 14, 2025

Search Tags:#data visualization #matplotlib #journal submission #medical illustration #PRS compliance #CMYK

Ready to run

npx skills add https://github.com/shakestzd/prs-dataviz --skill prs-figure-creator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: PRS Figure Creator
Source: https://github.com/shakestzd/prs-dataviz/tree/main/.claude/skills/prs-figure-creator
Command: npx skills add https://github.com/shakestzd/prs-dataviz --skill prs-figure-creator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the creation of publication-quality figures compliant with the Plastic and Reconstructive Surgery (PRS) journal's strict requirements, ensuring professional, accessible, and print-ready visualizations.

Core Features & Use Cases

PRS Compliance: Ensures figures meet DPI, color mode (CMYK), and dimension standards.
Data-Driven Design: Adapts visualization choices based on data characteristics and design principles.
Iterative Refinement: Employs a visual feedback loop for high-quality, error-free outputs.
Use Case: Generate a figure showing treatment efficacy over time, ensuring it's formatted correctly for journal submission.

Quick Start

Use the PRS Figure Creator skill to generate a publication-ready figure from your data.

Frequently Asked Questions about PRS Figure Creator

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

FAQPage Schema

How do I create publication-ready medical figures that meet PRS journal submission requirements?▼

Publication-ready medical figures for PRS journal submission require strict adherence to DPI, CMYK color mode, and dimension standards. This skill automates generating high-resolution, print-ready visualizations through a deterministic 6-phase pipeline including data exploration and compliant export.

What is the best way to format matplotlib data visualizations for CMYK and 300 DPI TIFF export?▼

Formatting matplotlib data visualizations for CMYK and 300 DPI TIFF export requires applying constraint-based design decisions and iterative visual refinement. This skill handles compliant export with validation, ensuring outputs meet high-resolution and color mode standards for journal submission.

How does an iterative visual feedback loop improve medical illustration quality?▼

An iterative visual feedback loop improves medical illustration quality by applying visual inspection and critique after initial generation. This mechanism refines data-driven design choices and accessibility standards, ensuring error-free, professional outputs for Plastic and Reconstructive Surgery journal submissions.

Can I generate compliant figures from raw clinical data without manual formatting?▼

Yes, you can generate compliant figures from raw clinical data without manual formatting. The skill adapts visualization choices based on data characteristics during data exploration, automatically applying design principles to produce CMYK-ready, high-resolution TIFF files.

Does this figure generation approach support accessibility standards for medical illustrations?▼

Yes, this figure generation approach supports accessibility standards for medical illustrations. It leverages data-driven principles and accessibility standards throughout the constraint-based design and iterative refinement phases to ensure professional, compliant visualizations for PRS journal submission.