chart-style-guide

Apply consistent matplotlib and seaborn style settings to Python charts.

2|Updated Feb 1, 2026

Search Tags:#styling #visualization #matplotlib #seaborn #data-visualization #color-palette #chart-design

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npx skills add https://github.com/s23h/sql-agent --skill chart-style-guide

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Please help me install this Agent Skill.
Skill: chart-style-guide
Source: https://github.com/s23h/sql-agent/tree/main/.claude/skills/chart-style-guide
Command: npx skills add https://github.com/s23h/sql-agent --skill chart-style-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures a consistent, professional styling language for data visualizations across Python plots, saving time and improving readability.

Core Features & Use Cases

Standardized aesthetics for charts created with matplotlib and seaborn.
Reusable color palettes, fonts, and spacing conventions to preserve a cohesive look.
Quick templates and guidelines for common chart types (bar, line, donut) to speed up development.

Quick Start

Ensure your Python environment has matplotlib and seaborn installed, then apply the style guide at the start of your plotting scripts. Import the style module and call apply_chart_style() before creating charts.

Frequently Asked Questions about chart-style-guide

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

FAQPage Schema

How do I apply consistent styling across matplotlib and seaborn charts?▼

You can apply consistent chart styling by importing the style module and calling apply_chart_style() before creating your visualizations. This enforces reusable color palettes, fonts, and spacing conventions across matplotlib and seaborn plots.

What is the best way to standardize color palettes and fonts for Python data visualization?▼

Standardizing color palettes and fonts is achieved by applying a reusable visual language across your plots. This Skill provides predefined templates and helper functions to enforce cohesive chart design for bar, line, and donut charts.

Does this chart styling approach work for both bar and line charts in seaborn?▼

Yes, this chart styling approach works for bar, line, and donut charts in seaborn. It applies standardized aesthetics across various chart types to ensure a cohesive look and improve readability.

Do I need matplotlib and seaborn installed to use these chart design templates?▼

Yes, you need matplotlib and seaborn installed in your Python environment to use these chart design templates. The helper functions rely on both libraries to apply the standardized style settings.

How do I speed up development for common data visualization chart types?▼

You can speed up development for common data visualization chart types by using quick templates and guidelines provided by the style guide. These templates offer predefined aesthetics for bar, line, and donut charts to accelerate plotting.

Why should I use a standardized visual language for my Python data charts?▼

Using a standardized visual language for Python data charts saves time and improves readability. It ensures professional, consistent styling across all your matplotlib and seaborn visualizations by reusing palettes and spacing conventions.