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data-visualization

Generate publication-ready matplotlib, seaborn, and plotly visualizations with Python patterns.

1|Updated Feb 19, 2026
Search Tags:#accessibility#visualization#plotly#matplotlib#seaborn#data-visualization#chart-design
One-click install
npx skills add https://github.com/cyyeh/duckdb-data-agent --skill data-visualization-cyyeh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/cyyeh/duckdb-data-agent/tree/main/plugins/data/skills/data-visualization
Command: npx skills add https://github.com/cyyeh/duckdb-data-agent --skill data-visualization-cyyeh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps people turn raw data into clear, accurate, and publication-ready visualizations by recommending appropriate chart types, style conventions, and accessibility improvements while providing Python code patterns for common charting tasks.

Core Features & Use Cases

  • Chart selection guidance: Match data relationships to the right visualization (time series, distributions, comparisons, composition, correlations, maps).
  • Reusable Python patterns: Practical matplotlib, seaborn, and plotly examples for line charts, bar charts, histograms, heatmaps, small multiples, and interactive figures.
  • Design and accessibility: Colorblind-friendly palettes, typography and layout advice, annotation patterns, and a pre-publish accessibility checklist.
  • Use Case: Quickly produce a multiplot report and export both static PNGs for publications and interactive HTML for web dashboards.

Quick Start

Ask the skill to generate a publication-quality matplotlib time series chart from a dataframe named df with columns date, category, and value, include a legend, mean/median lines, and save as trend_chart.png.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I choose the right chart type for my data in Python?▼

Choosing the right chart type involves matching your data relationships to the appropriate visualization, such as time series, distributions, comparisons, composition, correlations, or maps. The skill provides actionable chart selection guidance for Python plotting.

How do I make matplotlib charts accessible and colorblind-friendly?▼

Making matplotlib charts accessible involves applying colorblind-friendly palettes, typography and layout conventions, and annotation patterns. The skill provides a pre-publish accessibility checklist and styling guidance to ensure your visualizations are clear.

What is the best way to create publication-quality figures with Python?▼

The best way to create publication-quality figures is by applying proper axis formatting, annotations, and export-ready outputs. The skill provides reusable Python patterns for static PNGs and interactive HTML suitable for high-quality reports.

Can I generate both static PNGs and interactive HTML charts for a dashboard?▼

You can generate both static PNGs and interactive HTML charts by using matplotlib for static exports and plotly for interactive dashboards. The skill provides ready-to-run Python patterns to produce multiplot reports and export both formats.

Does this approach work for exploratory data analysis and interactive figures?▼

This approach works for exploratory data analysis and interactive figures by providing practical seaborn and plotly examples for histograms, heatmaps, and small multiples. It supports creating both static publication figures and interactive web dashboards.

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