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设计与创意 · SKILL.md

chart-visualization

chart-visualization

【设计与创意】This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 25 available options, extracts parameters based on detailed specifications, and generates a chart image using a Python script.

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技能说明

GitHub

name: chart-visualization description: This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 25 available options, extracts parameters based on detailed specifications, and generates a chart image using a Python script.

Chart Visualization Skill

This skill provides a comprehensive workflow for transforming data into visual charts. It handles chart selection, parameter extraction, and image generation.

Workflow

To visualize data, follow these steps:

1. Intelligent Chart Selection

Analyze the user's data features to determine the most appropriate chart type. Use the following guidelines (and consult references/ for detailed specs):

  • Time Series: Use generate_line_chart (trends) or generate_area_chart (accumulated trends). Use generate_dual_axes_chart for two different scales.
  • Comparisons: Use generate_bar_chart (categorical) or generate_column_chart. Use generate_histogram_chart for frequency distributions.
  • Part-to-Whole: Use generate_pie_chart or generate_treemap_chart (hierarchical).
  • Relationships & Flow: Use generate_scatter_chart (correlation), generate_sankey_chart (flow), or generate_venn_chart (overlap).
  • Maps: Use generate_district_map (regions), generate_pin_map (points), or generate_path_map (routes).
  • Hierarchies & Trees: Use generate_organization_chart or generate_mind_map.
  • Specialized:
    • generate_radar_chart: Multi-dimensional comparison.
    • generate_funnel_chart: Process stages.
    • generate_liquid_chart: Percentage/Progress.
    • generate_word_cloud_chart: Text frequency.
    • generate_boxplot_chart or generate_violin_chart: Statistical distribution.
    • generate_network_graph: Complex node-edge relationships.
    • generate_fishbone_diagram: Cause-effect analysis.
    • generate_flow_diagram: Process flow.

2. Parameter Extraction

Once a chart type is selected, read the corresponding file in the references/ directory (e.g., references/generate_line_chart.md) to identify the required and optional fields. Extract the data from the user's input and map it to the expected args format.

3. Chart Generation

Invoke the scripts/generate.py script with a JSON payload.

Payload Format:

{
  "tool": "generate_chart_type_name",
  "args": {
    "data": [...],
    "title": "...",
    "theme": "...",
    "style": { ... }
  }
}

Execution Command:

python ./scripts/generate.py '<payload_json>'

4. Result Return

The script will output the URL of the generated chart image. Return the following to the user:

  • The image URL.
  • The complete args (specification) used for generation.

Reference Material

Detailed specifications for each chart type are located in the references/ directory. Consult these files to ensure the args passed to the script match the expected schema.

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