visualization

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

1. Read the data or analysis results specified in $ARGUMENTS. Understand the variables, their types, and the story the data tells.
2. 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
3. Select a library or approach:
   • Python: matplotlib, seaborn, plotly
   • JavaScript: D3.js, Chart.js
   • CLI: gnuplot, termgraph for quick terminal output
4. Generate the visualization:
   • Set clear title and axis labels
   • Add legend if multiple series
   • Use colorblind-friendly palettes
   • Include data source annotation
5. 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
6. 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]