---

name: decision-visualization description: Decision-specific visualization skill for creating clear, actionable visual representations of analyses allowed-tools:

• Read
• Write
• Glob
• Grep
• Bash metadata: specialization: decision-intelligence domain: business category: visualization priority: high tools-libraries:
  • plotly
  • bokeh
  • matplotlib
  • d3.js

---

Decision Visualization

Overview

The Decision Visualization skill provides specialized visualization capabilities for decision support, creating clear, actionable visual representations that communicate analysis results effectively to decision-makers and stakeholders.

Capabilities

• Decision tree diagrams
• Strategy tables and consequence matrices
• Trade-off scatter plots
• Value-of-information graphs
• Confidence/uncertainty bands
• Waterfall charts for sensitivity
• Heat maps for MCDA
• Interactive dashboards

Used By Processes

• Executive Dashboard Development
• Structured Decision Making Process
• Multi-Criteria Decision Analysis (MCDA)
• Decision Documentation and Learning

Usage

Decision Tree Visualization

# Decision tree diagram configuration
decision_tree_viz = {
    "type": "decision_tree",
    "data": decision_tree_structure,
    "options": {
        "node_shapes": {
            "decision": "square",
            "chance": "circle",
            "terminal": "triangle"
        },
        "show_probabilities": True,
        "show_payoffs": True,
        "highlight_optimal_path": True,
        "color_scheme": "sequential",
        "orientation": "horizontal"
    }
}

Strategy Table

# Strategy comparison table
strategy_table = {
    "type": "strategy_table",
    "alternatives": ["Strategy A", "Strategy B", "Strategy C"],
    "criteria": ["Cost", "Time", "Quality", "Risk"],
    "data": performance_matrix,
    "options": {
        "color_coding": "performance_based",
        "show_weights": True,
        "show_scores": True,
        "highlight_winner": True
    }
}

Trade-off Scatter Plot

# Multi-objective trade-off visualization
tradeoff_plot = {
    "type": "scatter",
    "data": alternatives_data,
    "x_axis": {"variable": "cost", "label": "Total Cost ($)"},
    "y_axis": {"variable": "benefit", "label": "Expected Benefit"},
    "options": {
        "show_pareto_frontier": True,
        "label_alternatives": True,
        "size_by": "probability",
        "color_by": "risk_category",
        "show_dominated_region": True
    }
}

Tornado Diagram

# Sensitivity tornado diagram
tornado = {
    "type": "tornado",
    "base_value": 1000000,
    "sensitivities": {
        "Price": {"low": 800000, "high": 1300000},
        "Volume": {"low": 900000, "high": 1150000},
        "Cost": {"low": 950000, "high": 1100000},
        "Market Share": {"low": 850000, "high": 1200000}
    },
    "options": {
        "sort_by": "swing",
        "show_base_line": True,
        "color_scheme": ["red", "green"],
        "show_values": True
    }
}

Uncertainty Visualization

# Distribution and confidence visualization
uncertainty_viz = {
    "type": "distribution",
    "data": simulation_results,
    "options": {
        "show_histogram": True,
        "show_density": True,
        "show_percentiles": [5, 25, 50, 75, 95],
        "show_mean": True,
        "confidence_band": 0.90,
        "highlight_threshold": 0  # e.g., breakeven
    }
}

Visualization Types

Type	Use Case	Key Features
Decision Tree	Structure visualization	Nodes, branches, payoffs
Strategy Table	Alternative comparison	Color-coded performance
Tornado Diagram	Sensitivity ranking	Horizontal bars, swing
Spider/Radar	Multi-criteria profile	Polygon overlay
Heat Map	Matrix data	Color intensity
Waterfall	Value decomposition	Sequential bars
Scatter	Trade-offs	Points, Pareto frontier
Box Plot	Uncertainty	Quartiles, outliers
Fan Chart	Forecast uncertainty	Widening confidence bands

Input Schema

{
  "visualization_type": "string",
  "data": "object",
  "axes": {
    "x": {"variable": "string", "label": "string"},
    "y": {"variable": "string", "label": "string"}
  },
  "options": {
    "title": "string",
    "color_scheme": "string",
    "interactive": "boolean",
    "annotations": ["object"],
    "export_format": "png|svg|pdf|html"
  }
}

Output Schema

{
  "visualization_path": "string",
  "interactive_url": "string (if applicable)",
  "metadata": {
    "type": "string",
    "dimensions": {"width": "number", "height": "number"},
    "data_summary": "object"
  },
  "accessibility": {
    "alt_text": "string",
    "data_table": "object"
  }
}

Design Principles

1. Clarity: Remove chart junk, maximize data-ink ratio
2. Accuracy: No distortion, appropriate scales
3. Efficiency: Quick comprehension, key insights prominent
4. Actionability: Clear implications for decisions
5. Accessibility: Color-blind friendly, alt text provided

Best Practices

1. Match visualization type to data and message
2. Use consistent color schemes across related charts
3. Include clear titles and axis labels
4. Highlight key takeaways with annotations
5. Provide interactive features for exploration
6. Export to multiple formats for different uses
7. Include data tables for accessibility

Integration Points

• Receives data from all analysis skills
• Feeds into Data Storytelling for narratives
• Supports Executive Dashboard Development
• Connects with Decision Journal for documentation