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data-viz-plots

Generate publication-quality plots from structured data using Python's matplotlib and seaborn.

Updated Mar 17, 2026
Search Tags:#visualization#data-analysis#matplotlib#seaborn#publication-quality#local-execution#plots
One-click install
npx skills add https://github.com/jesusalainmm/ilumno_automatizaciones --skill data-viz-plots-jesusalainmm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-viz-plots
Source: https://github.com/jesusalainmm/ilumno_automatizaciones/tree/main/.claude/skills/data-viz-plots
Command: npx skills add https://github.com/jesusalainmm/ilumno_automatizaciones --skill data-viz-plots-jesusalainmm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create publication-quality plots and visualizations locally using matplotlib and seaborn, avoiding cloud dependencies and enabling reproducible figures for scientific work.

Core Features & Use Cases

  • Versatile visualizations: scatter, line, heatmap, box/violin plots, and multi-panel layouts.
  • Publication-ready styling: configurable palettes, fonts, and DPI for print-quality images.
  • Local execution & portability: runs entirely on your machine with standard Python environments, compatible with any LLM provider.

Quick Start

Provide your data as a pandas DataFrame and run the plotting workflow to generate publication-ready figures locally.

Frequently Asked Questions about data-viz-plots

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

FAQPage Schema
How do I generate publication-quality plots locally without cloud dependencies?▼

You generate publication-quality plots locally by running matplotlib and seaborn directly on your machine to produce high-resolution, print-ready images from structured data without relying on cloud services.

Can I create multi-panel comparisons across groups using matplotlib and seaborn?▼

Yes, you can create multi-panel comparisons across groups and conditions using this local plotting workflow, which supports versatile visualizations including scatter, line, box, violin, and heatmap layouts.

Does this plotting workflow support configurable palettes and fonts for scientific figures?▼

Yes, this workflow supports publication-ready styling with configurable palettes, fonts, and DPI settings, allowing you to export high-resolution images suitable for scientific papers and presentations.

What is the best way to export high-resolution images suitable for publication from a pandas DataFrame?▼

The best way to export high-resolution images is to provide your data as a pandas DataFrame and run the local plotting workflow, which renders and exports print-quality figures with configurable DPI using standard Python libraries.

Do I need a specific LLM provider to run matplotlib and seaborn visualizations?▼

No, local execution runs entirely on your machine with standard Python environments, making the plotting workflow compatible with any LLM provider without requiring a specific cloud dependency.

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