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plotting-library

Generate publication-ready charts from runnable matplotlib templates with a unified style.

1|1|Updated May 9, 2026
Search Tags:#workflow#templates#catalog#plotting#matplotlib#data-visualization
One-click install
npx skills add https://github.com/cupcake777/viz-skills --skill plotting-library
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plotting-library
Source: https://github.com/cupcake777/viz-skills/tree/main/plotting-library
Command: npx skills add https://github.com/cupcake777/viz-skills --skill plotting-library

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Standardizes and accelerates scientific plotting by providing a library of runnable plot templates and a shared matplotlib style, so researchers can generate publication-quality figures without rewriting boilerplate code.

Core Features & Use Cases

  • Self-contained templates: each chart template includes generate_mock_data(), plot(), and a main entry, enabling quick demos and reproducible figures.
  • Catalog-driven discovery: the /plotting-library/catalog.yaml exposes charts and metadata, allowing dynamic gallery generation without code changes.
  • Style consistency: a centralized matplotlibrc style file ensures plots follow Nature-inspired aesthetics across templates.
  • Use Case: quickly generate a volcano plot from differential expression data, or a heatmap from an expression matrix, by selecting a template and pointing to your data.

Quick Start

Run a demo template, for example python templates/volcano.py to generate a sample volcano plot.

Frequently Asked Questions about plotting-library

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

FAQPage Schema
How do I create publication-ready matplotlib charts without rewriting boilerplate code?▼

You can create publication-ready matplotlib charts by executing runnable plotting templates that enforce a unified style. Each template includes generate_mock_data() and plot() functions to quickly generate reproducible figures.

Can I generate a volcano plot or heatmap from differential expression data quickly?▼

Yes, you can generate a volcano plot or heatmap by selecting a template and pointing it to your data. Run a demo like python templates/volcano.py to produce sample publication-quality visualizations.

What Python environment do I need to run these data visualization templates?▼

These data visualization templates require a Python 3.9+ environment with matplotlib, numpy, and pandas installed. You need this specific setup to execute the plotting logic and generate charts.

How does the catalog-driven discovery mechanism work for finding plot templates?▼

The catalog-driven discovery mechanism uses a catalog.yaml file to expose chart templates and metadata. This allows you to dynamically generate a gallery of available plots without modifying the underlying code.

What is the best way to ensure style consistency across multiple scientific charts?▼

The best way to ensure style consistency is using a centralized matplotlibrc style file. It enforces Nature-inspired aesthetics across all templates so your scientific charts maintain a unified visual style.

Are these plotting templates self-contained for reproducible figure generation?▼

Yes, these plotting templates are fully self-contained. Each chart template includes a __main__ entry point alongside generate_mock_data() and plot() logic, enabling quick demos and reproducible figure generation.

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