--- name: sensitivity-analyzer-e41b6c33b89f description: Sensitivity analysis skill for identifying critical inputs and understanding model behavior under uncertainty allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: decision-intelligence domain: business category: simulation priority: high shared-candidate: true tools-libraries: - SALib - openturns - sensitivity - numpy graph: domains: [domain:business-intelligence] skillAreas: [skill-area:quantitative-modeling, skill-area:statistical-analysis, skill-area:financial-modeling] roles: [role:data-scientist, role:quantitative-analyst, role:financial-analyst] --- # Sensitivity Analyzer ## Overview The Sensitivity Analyzer skill provides comprehensive capabilities for identifying critical inputs and understanding how model outputs respond to parameter changes. It supports both local (one-at-a-time) and global sensitivity analysis methods, enabling robust decision-making under uncertainty. ## Capabilities - One-at-a-time (OAT) sensitivity - Global sensitivity analysis (Sobol indices, Morris screening) - Tornado diagram generation - Spider plot creation - Parameter importance ranking - Threshold identification - Breakeven analysis - Scenario comparison ## Used By Processes - Monte Carlo Simulation for Decision Support - Multi-Criteria Decision Analysis (MCDA) - Prescriptive Analytics and Optimization - What-If Analysis Framework ## Usage ### One-at-a-Time (OAT) Analysis ```python # Define OAT analysis oat_config = { "base_case": { "price": 100, "volume": 10000, "cost": 60, "fixed_costs": 200000 }, "variations": { "price": {"range": [-20, 20], "step": 5, "unit": "%"}, "volume": {"range": [-30, 30], "step": 10, "unit": "%"}, "cost": {"range": [-15, 15], "step": 5, "unit": "%"}, "fixed_costs": {"range": [-10, 10], "step": 5, "unit": "%"} }, "output_variable": "profit" } ``` ### Global Sensitivity (Sobol Indices) ```python # Define Sobol analysis sobol_config = { "parameters": { "price": {"bounds": [80, 120], "distribution": "uniform"}, "volume": {"bounds": [7000, 13000], "distribution": "uniform"}, "cost": {"bounds": [50, 70], "distribution": "uniform"} }, "sample_size": 10000, "calculate_second_order": True } ``` ### Morris Screening Efficient screening method for many parameters: - Identifies parameters with negligible effects - Distinguishes linear vs. non-linear effects - Detects interaction effects ### Sensitivity Indices | Index | Meaning | |-------|---------| | S1 (First-order) | Direct effect of parameter | | ST (Total) | Direct + all interaction effects | | S2 (Second-order) | Pairwise interaction effect | ## Visualization Types 1. **Tornado Diagram**: Horizontal bars showing impact range 2. **Spider Plot**: Lines showing output vs. % change in each input 3. **Scatter Plot**: Output vs. single input with trend line 4. **Sobol Bar Chart**: First-order and total indices comparison 5. **Morris Plot**: Mean vs. standard deviation of elementary effects ## Input Schema ```json { "analysis_type": "OAT|sobol|morris|breakeven", "model": "function or expression", "parameters": { "param_name": { "base_value": "number", "range": ["number", "number"], "distribution": "string" } }, "options": { "sample_size": "number", "output_variable": "string", "calculate_interactions": "boolean", "confidence_level": "number" } } ``` ## Output Schema ```json { "analysis_type": "string", "parameter_rankings": [ { "parameter": "string", "importance_score": "number", "effect_direction": "positive|negative", "first_order_index": "number", "total_index": "number" } ], "breakeven_points": { "parameter": { "breakeven_value": "number", "current_distance": "number" } }, "interactions": [ { "parameters": ["string", "string"], "interaction_index": "number" } ], "tornado_data": { "parameter": { "low_output": "number", "high_output": "number", "swing": "number" } }, "visualization_paths": ["string"] } ``` ## Best Practices 1. Start with Morris screening for many parameters (>10) 2. Use Sobol indices for detailed analysis of top parameters 3. Include parameter correlations when they exist 4. Report confidence intervals for sensitivity indices 5. Consider non-linear effects (total vs. first-order indices) 6. Communicate results using tornado diagrams for executives 7. Document parameter ranges and their justification ## Interpretation Guidelines ### Sobol Index Interpretation - **High S1, High ST**: Important direct effect - **Low S1, High ST**: Important through interactions - **High S1, Low ST-S1**: Few interactions - **Low ST**: Parameter can be fixed at nominal value ### Breakeven Analysis Identifies the parameter value where: - NPV = 0 - Profit = 0 - Decision changes - Threshold is crossed ## Integration Points - Receives model from Monte Carlo Engine - Feeds into Decision Visualization for charts - Supports MCDA methods for weight sensitivity - Connects with Real Options Analyzer for volatility impact --- 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, Sales Roles: Business Development, Data Analyst, Growth Marketer, Marketing Analyst, Product Manager Specialties: API Tools: Salib, Numpy, Openturns, Sensitivity Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 5d0840b6dd2584457a3d66004246b3cd6b351dce926283f81a24bd3b2403e2bc