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plot

Generate publication-quality plots from data using matplotlib and seaborn.

11|1|Updated Feb 10, 2026
Search Tags:#plot#data#visualization#matplotlib#seaborn#publication#figures
One-click install
npx skills add https://github.com/michaelrizvi/claude-config --skill plot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plot
Source: https://github.com/michaelrizvi/claude-config/tree/main/skills/plot
Command: npx skills add https://github.com/michaelrizvi/claude-config --skill plot

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This plotting skill helps researchers and engineers quickly generate publication-quality figures from data, saving time and ensuring consistent visuals.

Core Features & Use Cases

  • Automated plotting: produce training curves, bar charts, scatter plots, heatmaps, and multi-panel figures that are ready for papers.
  • Styling & accessibility: apply consistent styles, colorblind-friendly palettes, and legible axis labels for scientific figures.
  • Use Case: When preparing a manuscript, generate a complete figure with labeled axes and captions from a data frame.

Quick Start

Ask me to generate a publication-quality plot from your data using matplotlib and seaborn.

Frequently Asked Questions about plot

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

FAQPage Schema
How do I create publication-ready plots from a dataframe for a scientific paper?▼

You can generate publication-quality plots by providing your dataframe to be visualized as training curves, bar charts, scatter plots, or heatmaps using matplotlib and seaborn defaults with consistent styling.

Can I export matplotlib figures as vector formats like PDF or SVG?▼

Vector export formats like PDF and SVG are supported for generated figures, ensuring that scientific plots and multi-panel figures maintain high resolution for publication and presentation use.

Does this plotting tool support colorblind-friendly palettes for scientific figures?▼

Colorblind-friendly palettes are supported and applied automatically, ensuring that heatmaps, scatter plots, and bar charts remain visually accessible while maintaining consistent styling across all generated figures.

What is the best way to generate multi-panel figures for a research manuscript?▼

Generating multi-panel figures for manuscripts involves using automated plotting functions that combine multiple charts into a single publication-quality layout with legible axis labels and consistent visual styling.

How do I plot training curves with consistent styling across multiple figures?▼

Training curves are plotted with consistent styling by applying unified matplotlib and seaborn defaults across figures, ensuring that axis labels, legends, and colorblind-friendly palettes remain uniform throughout the report.

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