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data-viz-plots

Generate publication-quality plots with matplotlib and seaborn.

1.2k|145|Updated Mar 22, 2021
Search Tags:#EDA#plotting#matplotlib#local#seaborn#data-visualization#publication-quality
One-click install
npx skills add https://github.com/Starlitnightly/omicverse --skill data-viz-plots
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-viz-plots
Source: https://github.com/Starlitnightly/omicverse/tree/main/.claude/skills/data-viz-plots
Command: npx skills add https://github.com/Starlitnightly/omicverse --skill data-viz-plots

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables creating publication-quality visualizations using matplotlib and seaborn locally, enabling exploratory analysis and presentation-ready figures without relying on cloud services.

Core Features & Use Cases

  • Scatter, line, heatmaps, violin plots, and more for exploratory data analysis.
  • Multi-panel figures for comprehensive comparisons and presentations.
  • High-resolution exports suitable for papers and slides.
  • Styling and color palettes to align visuals with journals or branding.

Quick Start

Produce a publication-ready scatter plot from x_data and y_data and save as scatter_plot.png.

Frequently Asked Questions about data-viz-plots

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

FAQPage Schema
How do I create publication-quality plots with matplotlib and seaborn locally?▼

Publication-quality plots are generated using matplotlib and seaborn styling and color palettes applied to scatter, line, heatmap, box, violin, and bar plots. Export at high resolution as PNG or PDF for papers and slides without relying on cloud services.

Can I make multi-panel figures for exploratory data analysis and comparisons?▼

Yes, multi-panel layouts compose multiple plot types—scatter, heatmaps, violin plots—into comprehensive figures suitable for presenting clustering results, gene-expression data, and QC metrics side by side.

What plot types does matplotlib and seaborn support for data visualization?▼

Supported plot types include scatter, line, heatmap, box, violin, and bar plots. Each is configurable with publication-ready styling and palette adjustments to match journal or branding requirements.

How do I export high-resolution visualizations for papers and presentations?▼

High-resolution exports save plots as PNG or PDF files optimized for publication. Configure resolution, styling, and color palettes during generation to produce journal-ready figures directly from local Python environments.

What data formats and environments does local plot generation require?▼

A Python environment with matplotlib and seaborn is required. Input data is provided as arrays or dataframes; the Skill runs locally without external dependencies and exports publication-ready images.

Can I use this for gene expression and clustering visualization?▼

Yes, the Skill applies to gene-expression visualization, clustering results, and QC metric visualization. Multi-panel heatmaps, scatter plots, and box plots handle complex biological datasets with publication-grade styling.

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