data-viz

name	data-viz
description	Data visualization for charts and graphs. Use when user needs "画图/图表/可视化". Creates static PNG or interactive HTML charts from data.

Mental Model

Visualization is choosing the right chart to answer a specific question. Chart type depends on data relationship, not aesthetics.

Chart Selection

Question	Chart Type
Trends over time?	Line chart
Compare categories?	Bar chart
Show distribution?	Histogram, box plot
Relationship between variables?	Scatter plot
Parts of whole?	Pie, stacked bar
2D patterns?	Heatmap
Financial data?	Candlestick, OHLC

Anti-Patterns (NEVER)

• Don't use Chinese characters anywhere in charts - use English for labels, titles, legends, data labels
• Don't use Chinese characters in filenames (ASCII only)
• Don't pick wrong chart type for the question
• Don't overload with data → aggregate or sample
• Don't forget labels, title, legend
• Don't use poor colors (colorblind-safe palettes)

Chart language: Always use English (titles, axes, legends, labels) to avoid font rendering issues.

Output Formats

• PNG: Static, high-quality for reports
• HTML: Interactive (zoom, pan, hover)
• SVG: Vector for editing

Filename: {chart_type}_{timestamp}.{ext} (ASCII only)

Workflow

1. Ask: What's the question? What story to tell?
2. Load data from CSV/JSON (or data-analysis output)
3. Choose chart type based on question
4. Create Python script and execute using virtual environment:

   .venv/bin/python script.py
   

5. Return file path to user

Python Environment

Auto-initialize virtual environment if needed, then execute:

# Navigate to skill directory
cd skills/data-viz

# Auto-create venv if not exists
if [ ! -f ".venv/bin/python" ]; then
    echo "Creating Python environment..."
    ./setup.sh
fi

# Execute script
.venv/bin/python your_script.py

The setup script auto-installs: matplotlib, seaborn, plotly, pandas with Chinese font support.

References (load on demand)

For chart APIs and code templates, load: references/REFERENCE.md, references/templates.md