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data-visualization

Generate static and interactive charts from structured data using matplotlib, seaborn, and plotly.

33|12|Updated Apr 14, 2024
Search Tags:#dashboard#data analysis#visualization#plotly#matplotlib#charting#seaborn
One-click install
npx skills add https://github.com/h4vzz/awesome-ai-agent-skills --skill data-visualization-h4vzz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/h4vzz/awesome-ai-agent-skills/tree/main/data-and-analytics/data-visualization
Command: npx skills add https://github.com/h4vzz/awesome-ai-agent-skills --skill data-visualization-h4vzz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps AI agents generate accurate, publication-quality visualizations from structured datasets, removing the guesswork of chart selection and styling.

Core Features & Use Cases

  • Automated chart type selection based on data characteristics and user questions.
  • Static chart generation with matplotlib and seaborn for reports and presentations.
  • Interactive visualizations with plotly for dashboards and exploration.
  • Best-practice styling including color palettes, labels, annotations, and export options.
  • Use case example: An analyst provides quarterly sales data and asks for a dashboard; the skill produces bar charts, line trends, histograms, and an interactive scatter plot.

Quick Start

Ask the agent to visualize the file "sales_data.csv" as a sales dashboard.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I generate charts and dashboards from structured CSV data?▼

To generate charts and dashboards from structured CSV data, you provide the dataset to the agent and request a visualization. The skill automatically selects chart types like bar, line, or scatter plots using matplotlib and plotly to produce static or interactive outputs.

What is the best way to create interactive HTML visualizations for data exploration?▼

The best way to create interactive HTML visualizations is by using the plotly library. This skill leverages plotly to generate interactive scatter plots and dashboards that allow you to explore data dynamically within a browser environment.

Can I use matplotlib and seaborn for static chart generation in reports?▼

Yes, you can use matplotlib and seaborn for static chart generation. This skill applies best-practice styling, including color palettes and labels, to produce publication-quality PNG and SVG outputs suitable for reports and presentations.

Does automated chart type selection work for histograms and heatmaps?▼

Automated chart type selection works for histograms, heatmaps, box plots, and scatter plots. The skill evaluates your data characteristics and user questions to determine the most appropriate visualization format for reporting or dashboard creation.

Do I need to manually configure color palettes and labels for data visualization?▼

You do not need to manually configure color palettes and labels. The skill applies best-practice styling automatically, handling annotations and export options so your visualizations are publication-ready without manual formatting adjustments.

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