Decision Visualization
SkillDev toolsDecision-specific visualization skill for creating clear, actionable visual representations of analyses
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Decision Visualization skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/decision-intelligence/skills/decision-visualization/SKILL.md and read by ahel’s review.
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
- Clarity: Remove chart junk, maximize data-ink ratio
- Accuracy: No distortion, appropriate scales
- Efficiency: Quick comprehension, key insights prominent
- Actionability: Clear implications for decisions
- Accessibility: Color-blind friendly, alt text provided
Best Practices
- Match visualization type to data and message
- Use consistent color schemes across related charts
- Include clear titles and axis labels
- Highlight key takeaways with annotations
- Provide interactive features for exploration
- Export to multiple formats for different uses
- 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
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
decision-visualization- Source
- github.com/a5c-ai/babysitter
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