--- name: decision-visualization-8357c7d4c0be 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 graph: domains: [domain:business-intelligence] skillAreas: [skill-area:data-analytics, skill-area:data-visualization, skill-area:strategic-analysis] roles: [role:data-analyst, role:bi-developer, role:strategic-planner] --- # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```python # 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 ```json { "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 ```json { "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 --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Marketing, Product Management, Project & Program Management, Sales Roles: Business Development, Data Analyst, Marketing Analyst, Product Manager, Project Manager Specialties: API, Cross Platform, Design, Frontend Tools: Bokeh, Matplotlib, Plotly, D3.Js Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 82582485a59709c612385838588b0656744f4e0fcb32b239a5ae8d326bd2d4b5