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data-visualization

This skill generates charts and visualizations using multiple libraries including Matplotlib, Seaborn, Plotly, and Altair, supporting line charts, bar charts, pie charts, scatter plots, histograms, box plots, and heatmaps. Use it when users need to create static or interactive visualizations, transform raw data into graphical representations, or export charts in formats like PNG, SVG, or PDF.

Ver fuente Repositorio: marketplace

Instalar en Claude Code

Copiar

git clone --depth 1 https://github.com/aiskillstore/marketplace /tmp/data-visualization && cp -r /tmp/data-visualization/skills/0xkynz/data-visualization ~/.claude/skills/data-visualization

Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

Definición

SKILL.md

# Data Visualization Skill

This skill provides capabilities for creating data visualizations.

## Quick Start

Using matplotlib for basic charts:

```python
import matplotlib.pyplot as plt

# Simple line chart
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.title("Sample Chart")
plt.savefig("chart.png")
```

## Capabilities

### Chart Types
- Line charts
- Bar charts
- Pie charts
- Scatter plots
- Histograms
- Box plots
- Heatmaps

### Libraries Supported
- Matplotlib (static charts)
- Seaborn (statistical visualizations)
- Plotly (interactive charts)
- Altair (declarative visualization)

### Advanced Features
- Multi-axis plots
- Subplots and grids
- Custom themes and styling
- Annotations and labels
- Export to various formats (PNG, SVG, PDF)

## Best Practices

1. Choose the right chart type for your data
2. Use clear labels and titles
3. Consider color accessibility
4. Keep visualizations simple and focused
5. Export at appropriate resolution for intended use

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