data-visualization
Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
Best use case
data-visualization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create charts, graphs, and visualizations from data. Use when the user needs to visualize data, create charts, or generate reports with graphics.
Teams using data-visualization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
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 | Not specified | 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.
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 useRelated Skills
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