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matplotlib

Create publication-ready Matplotlib figures from tabular, array, and time-series data.

Updated Jul 1, 2026
Search Tags:#styling#visualization#scientific#plotting#matplotlib#export#subplots
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill matplotlib-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matplotlib
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/matplotlib
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill matplotlib-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, matplotlib, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you turn raw data into precise, fully customized plots when default charting is too limited for scientific, technical, or publication work.

Core Features & Use Cases

  • Fine-grained plot control: Build line charts, scatter plots, bar charts, histograms, heatmaps, contour plots, box plots, violin plots, 3D figures, and multi-panel layouts with exact control over styling.
  • Publication and workflow support: Tune fonts, colors, legends, grids, spines, layout, and export settings to produce clean figures for papers, reports, notebooks, and presentations.
  • Use case: If you need to visualize training metrics, compare model runs, or assemble a complex scientific figure with shared axes and colorbars, this Skill provides the plotting structure and styling guidance.

Quick Start

Use the matplotlib skill to create a polished plot from your data and save it as a high-resolution figure file.

Frequently Asked Questions about matplotlib

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

FAQPage Schema
How do I create publication-ready scientific plots from array and time-series data?▼

Create publication-ready scientific plots by using Matplotlib to build line, scatter, bar, histogram, heatmap, and contour figures from array and time-series data with fine-grained styling control.

How do I assemble multi-panel figures with shared axes and colorbars?▼

Assemble multi-panel figures by composing Matplotlib subplots with layout control, shared axes, legends, and colorbars to build complex scientific visualizations for papers and reports.

Does this Skill support customizing fonts, legends, and spines for technical reporting?▼

Yes, this Skill supports customizing fonts, colors, legends, grids, spines, and layout via Matplotlib rcParams to produce clean, styled figures for notebooks and technical reports.

Can I generate 3D plots and violin plots using Matplotlib for model comparison?▼

Yes, you can generate 3D figures, violin plots, and box plots using Matplotlib to visualize training metrics and compare model runs with exact control over plot styling.

Do I need numpy and scipy installed to build complex scientific figures?▼

Yes, you need numpy, scipy, and matplotlib installed to process tabular, array, and time-series data into fully customized, publication-ready scientific figures.

What is the best way to export high-resolution Matplotlib figures for presentations?▼

The best way to export high-resolution figures is to use Matplotlib's export settings to tune layout and resolution, generating polished visual outputs for presentations and papers.

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