| name | visualization |
| description | Create data visualizations (charts, plots, dashboards). Triggers on 'visualize data', 'create chart', 'plot data', 'build dashboard'. NOT for written reports — use reporting. |
| allowed-tools | Bash, Read, Write |
When to Use
- Turning analysis results into charts or plots
- Creating dashboards to display KPIs and metrics
- Comparing distributions, trends, or compositions visually
- Generating visual artifacts for inclusion in reports or presentations
When NOT to Use
- You need a written narrative report — use reporting
- Data has not been analyzed yet — use data-analysis first
- You need to collect raw data — use data-collection
Instructions
- Read the data or analysis results specified in
$ARGUMENTS. Understand the variables, their types, and the story the data tells.
- Determine the appropriate chart type based on data characteristics:
- Trend over time -> line chart
- Comparison across categories -> bar chart (horizontal for many categories)
- Part-of-whole composition -> pie chart or stacked bar
- Distribution of values -> histogram or box plot
- Relationship between variables -> scatter plot
- Geographic data -> map or choropleth
- Select a library or approach:
- Python: matplotlib, seaborn, plotly
- JavaScript: D3.js, Chart.js
- CLI: gnuplot, termgraph for quick terminal output
- Generate the visualization:
- Set clear title and axis labels
- Add legend if multiple series
- Use colorblind-friendly palettes
- Include data source annotation
- Annotate with context:
- Highlight key data points or thresholds
- Add trend lines or reference lines where useful
- Include summary statistics on the chart if space permits
- Save output files and report what was created.
Output
## Visualizations Created
### Charts
| Chart | Type | Data | File |
|-------|------|------|------|
| ... | ... | ... | ... |
### Design Notes
- Color palette: [name]
- Library: [tool used]
- Interactive: [yes/no]
