multiAI Summary Pending
data-visualization
Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
231 stars
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/data-visualization/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/0xkynz/data-visualization/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-visualization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-visualization Compares
| Feature / Agent | data-visualization | Standard Approach |
|---|---|---|
| Platform Support | multi | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
Which AI agents support this skill?
This skill is compatible with multi.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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