chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [business-context] | --market-expansion | --product-launch | --funding-scenarios
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description: Explore multiple business timeline scenarios with comprehensive risk analysis and strategic optimization
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---
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# Business Scenario Explorer
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Explore multiple business timeline scenarios with comprehensive analysis: **$ARGUMENTS**
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## Current Business Context
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- Business model: Based on $ARGUMENTS analysis or existing documentation
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- Market conditions: @README.md or business documentation
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- Financial data: Historical performance and current metrics
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- Competitive landscape: Industry analysis and positioning
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## Task
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Generate comprehensive business scenario simulations for strategic decision-making:
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**Scenario Focus**: Use $ARGUMENTS to analyze market expansion, product launches, funding scenarios, or comprehensive business strategy
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**Scenario Framework**:
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1. **Baseline Scenario** - Most likely trajectory based on current performance and market conditions
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2. **Optimistic Scenarios** - Best-case outcomes with favorable market conditions and successful execution
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3. **Pessimistic Scenarios** - Adverse conditions, increased competition, and execution challenges
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4. **Disruption Scenarios** - Technology breakthroughs, new entrants, and black swan events
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5. **Constraint Analysis** - Resource limitations, regulatory factors, and operational boundaries
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6. **Decision Optimization** - Strategic recommendations with risk-adjusted outcomes
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**Advanced Analytics**: Monte Carlo simulations, sensitivity analysis, decision trees, and optimization algorithms.
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**Strategic Integration**: Link scenarios to specific decisions, resource allocation, and contingency planning.
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**Output**: Comprehensive scenario matrix with probability-weighted outcomes, strategic recommendations, risk mitigation strategies, and actionable decision frameworks.
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [constraint-domain] | --business | --technical | --regulatory | --resource
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description: Model system constraints with validation, dependency mapping, and optimization strategies
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---
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# Constraint Modeler
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Model comprehensive system constraints with systematic validation and optimization: **$ARGUMENTS**
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## Current System Context
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- Domain scope: Based on $ARGUMENTS (business, technical, operational, financial)
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- Existing constraints: @documentation or configuration files
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- System boundaries: Current limitations and dependencies
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- Change dynamics: Historical constraint evolution patterns
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## Task
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Create comprehensive constraint models for accurate simulation and decision-making:
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**Constraint Domain**: Use $ARGUMENTS to focus on business, technical, regulatory, or resource constraints
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**Constraint Framework**:
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1. **Hard Constraints** - Absolute limits that cannot be violated (legal, physical, technical)
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2. **Soft Constraints** - Preferences and trade-offs that can be managed (budget, quality, timing)
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3. **Dynamic Constraints** - Limitations that evolve over time (market, technology, capacity)
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4. **Constraint Dependencies** - Relationships and interactions between different limitations
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5. **Validation Framework** - Methods to verify constraint accuracy and relevance
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6. **Optimization Strategies** - Approaches to relax, substitute, or circumvent constraints
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**Advanced Analysis**: Constraint sensitivity analysis, bottleneck identification, scenario boundary definition, and optimization algorithms.
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**Strategic Application**: Link constraint models to decision scenarios, resource allocation, and strategic planning.
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**Output**: Complete constraint model with interaction matrices, validation reports, optimization recommendations, and scenario boundary definitions.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [decision-context] | --strategic | --investment | --operational | --crisis-response
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description: Explore complex decision branches with probability analysis, expected value calculation, and optimization
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---
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# Decision Tree Explorer
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Explore complex decision scenarios with comprehensive probability analysis and optimization: **$ARGUMENTS**
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## Current Decision Context
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- Decision scope: Based on $ARGUMENTS (strategic, investment, operational, crisis response)
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- Available options: Current alternatives under consideration
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- Success criteria: Key metrics for decision evaluation
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- Resource constraints: Limitations affecting available choices
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## Task
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Create comprehensive decision tree analysis for optimal choice selection:
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**Decision Context**: Use $ARGUMENTS to analyze strategic decisions, investments, operations, or crisis responses
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**Decision Framework**:
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1. **Option Generation** - Comprehensive alternative identification including hybrid and innovative approaches
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2. **Probability Assessment** - Systematic likelihood estimation using base rates, expert judgment, and market data
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3. **Expected Value Analysis** - Multi-dimensional value calculation including financial, strategic, and risk factors
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4. **Sensitivity Analysis** - Critical assumption testing and break-even analysis
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5. **Risk Assessment** - Comprehensive risk identification, impact analysis, and mitigation strategies
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6. **Optimization Engine** - Multi-criteria decision analysis with stakeholder preference integration
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**Advanced Analytics**: Monte Carlo simulations, real options valuation, decision path optimization, and robustness testing.
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**Implementation Integration**: Connect analysis to specific actions, success metrics, and contingency planning.
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**Output**: Complete decision tree with probability-weighted outcomes, expected value calculations, risk assessments, and strategic recommendations with implementation guidance.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [twin-subject] | --manufacturing | --business-process | --customer-journey | --system-performance
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description: Create calibrated digital twins with real-world validation, scenario testing, and decision optimization
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---
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# Digital Twin Creator
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Create comprehensive digital twins with systematic calibration and validation: **$ARGUMENTS**
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## Current System State
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- Twin subject: Based on $ARGUMENTS (manufacturing, business process, customer journey, system performance)
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- Available data: Existing datasets, sensors, monitoring systems, and historical records
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- System boundaries: Components, interfaces, and environmental factors to model
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- Decision requirements: Specific use cases and accuracy needs for the digital twin
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## Task
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Build production-ready digital twin with comprehensive modeling and calibration:
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**Twin Subject**: Use $ARGUMENTS to model manufacturing systems, business processes, customer journeys, or system performance
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**Digital Twin Architecture**:
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1. **System Mapping** - Component identification, relationship modeling, and boundary definition
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2. **Data Foundation** - Quality assessment, gap analysis, and validation framework
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3. **Model Construction** - Behavior modeling, interaction dynamics, and environmental factors
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4. **Calibration Engine** - Historical validation, real-time adjustment, and accuracy monitoring
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5. **Scenario Simulation** - What-if testing, optimization scenarios, and stress testing
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6. **Decision Integration** - Recommendation engine, optimization algorithms, and risk assessment
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**Advanced Features**: Real-time synchronization, predictive analytics, automated parameter tuning, and continuous learning.
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**Quality Assurance**: Validation metrics, confidence intervals, model drift detection, and performance monitoring.
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**Output**: Production-ready digital twin with calibration reports, scenario testing capabilities, decision support features, and comprehensive documentation.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [time-horizon] | --near-term | --medium-term | --long-term | --disruption-focus
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description: Generate comprehensive future scenarios with plausibility scoring, trend integration, and strategic implications
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---
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# Future Scenario Generator
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Generate comprehensive future scenarios with systematic analysis and strategic integration: **$ARGUMENTS**
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## Current Trend Context
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- Time horizon: Based on $ARGUMENTS (1-2 years, 3-5 years, 5-10+ years)
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- Domain focus: Industry, technology, society, or economic scenario generation
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- Existing trends: Current patterns, trajectories, and emerging developments
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- Key variables: Major factors that could shape future outcomes
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## Task
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Create systematic future scenarios with comprehensive analysis and strategic implications:
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**Time Horizon**: Use $ARGUMENTS to focus on near-term, medium-term, long-term, or disruption-focused scenarios
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**Scenario Framework**:
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1. **Trend Analysis** - Multi-dimensional trend identification across technology, social, economic, and regulatory domains
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2. **Scenario Architecture** - Baseline, optimistic, pessimistic, and transformation scenarios with cross-impact analysis
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3. **Plausibility Assessment** - Multi-criteria scoring based on historical precedent, logical consistency, and expert validation
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4. **Wild Card Integration** - Low-probability, high-impact events and disruption modeling
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5. **Strategic Implications** - Decision-relevant insights and robust strategy identification
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6. **Monitoring Framework** - Early warning indicators and scenario tracking systems
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**Advanced Features**: Monte Carlo simulations, scenario interaction modeling, confidence intervals, and adaptive scenario management.
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**Decision Integration**: Connect scenarios to strategic planning, risk management, and option generation with actionable recommendations.
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**Output**: Comprehensive scenario portfolio with plausibility scores, strategic implications, monitoring indicators, and decision frameworks for multiple future possibilities.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [market-trigger] | --product-launch | --pricing-change | --marketing-campaign | --competitive-response
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description: Model comprehensive market and customer responses with segment analysis, behavioral prediction, and optimization
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---
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# Market Response Modeler
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Model comprehensive market and customer responses with advanced behavioral prediction: **$ARGUMENTS**
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## Current Market Context
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- Market definition: Based on $ARGUMENTS (target segments, geographic scope, competitive landscape)
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- Response trigger: Product launch, pricing change, marketing campaign, or competitive response
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- Available data: Customer behavior data, market research, and historical response patterns
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- Success metrics: Key performance indicators for measuring response effectiveness
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## Task
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Create comprehensive market response simulation with predictive analytics and optimization:
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**Market Trigger**: Use $ARGUMENTS to model responses to product launches, pricing changes, marketing campaigns, or competitive actions
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**Response Framework**:
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1. **Market Segmentation** - Comprehensive segment analysis with behavioral, demographic, and needs-based categorization
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2. **Response Behavior Modeling** - Customer journey mapping, response driver analysis, and intensity prediction
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3. **Competitive Response Integration** - Competitor reaction modeling and market dynamic effects
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4. **Response Simulation Engine** - Multi-scenario testing with timeline modeling and probability assessment
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5. **Prediction Algorithms** - Statistical modeling, machine learning, and expert system integration
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6. **Response Optimization** - Message, offering, channel, and timing optimization strategies
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**Advanced Analytics**: Monte Carlo simulations, competitive game theory, behavioral economics integration, and real-time calibration.
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**Decision Support**: Strategic recommendations with segment-specific tactics, risk mitigation, and success measurement frameworks.
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**Output**: Complete market response prediction with segment analysis, optimization recommendations, competitive scenarios, and implementation guidelines for maximum market impact.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [simulation-target] | --financial-projections | --project-timelines | --market-scenarios | --risk-assessment
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description: Run Monte Carlo simulations with probability distributions, confidence intervals, and statistical analysis
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---
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# Monte Carlo Simulator
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Run comprehensive Monte Carlo simulations with advanced statistical analysis: **$ARGUMENTS**
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## Current Analysis Context
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- Simulation target: Based on $ARGUMENTS (financial projections, project timelines, market scenarios, risk assessment)
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- Key variables: Uncertain parameters that drive outcome variability
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- Available data: Historical data, expert estimates, and probability distributions
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- Decision requirements: Confidence levels and risk tolerance for decision-making
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## Task
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Execute sophisticated Monte Carlo simulations with comprehensive uncertainty quantification:
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**Simulation Target**: Use $ARGUMENTS to simulate financial projections, project timelines, market scenarios, or risk assessments
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**Monte Carlo Framework**:
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1. **Variable Definition** - Uncertain parameter identification, probability distribution selection, and correlation modeling
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2. **Simulation Engine** - Random sampling, scenario generation, and statistical convergence analysis
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3. **Output Analysis** - Probability distributions, confidence intervals, and sensitivity analysis
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4. **Risk Quantification** - Value at Risk (VaR), extreme scenario analysis, and tail risk assessment
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5. **Scenario Clustering** - Pattern recognition, outcome categorization, and decision-relevant grouping
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6. **Decision Integration** - Risk-adjusted recommendations, optimization strategies, and contingency planning
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**Advanced Features**: Latin hypercube sampling, copula modeling, importance sampling, and variance reduction techniques.
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**Statistical Rigor**: Convergence testing, goodness-of-fit validation, and robust statistical inference with comprehensive uncertainty bounds.
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**Output**: Complete Monte Carlo analysis with probability distributions, risk metrics, scenario analysis, and statistically-grounded decision recommendations with quantified confidence levels.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [simulation-type] | --business | --technical | --behavioral | --strategic
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description: Calibrate simulation accuracy with systematic validation, bias detection, and continuous improvement
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---
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# Simulation Calibrator
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Calibrate simulation accuracy with comprehensive validation and continuous improvement: **$ARGUMENTS**
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## Current Simulation State
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- Simulation type: Based on $ARGUMENTS (business, technical, behavioral, strategic simulation)
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- Accuracy requirements: Mission-critical (95%+), strategic (80-95%), or exploratory (50-70%)
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- Validation data: Historical outcomes, real-world benchmarks, and expert assessments
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- Performance metrics: Current accuracy levels and improvement opportunities
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## Task
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Implement systematic simulation calibration with comprehensive accuracy improvement:
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**Simulation Type**: Use $ARGUMENTS to calibrate business simulations, technical models, behavioral predictions, or strategic scenarios
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**Calibration Framework**:
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1. **Baseline Assessment** - Historical validation, accuracy metrics, and error pattern analysis
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2. **Bias Detection** - Systematic identification of cognitive, data, and model biases with mitigation strategies
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3. **Validation Loops** - Multi-level validation with internal consistency, expert review, and empirical testing
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4. **Real-Time Calibration** - Continuous monitoring, automated adjustments, and adaptive learning integration
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5. **Quality Assurance** - Meta-calibration assessment and improvement sustainability
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6. **Improvement Roadmap** - Systematic enhancement strategies with performance tracking
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**Advanced Features**: Automated bias detection, machine learning calibration, cross-simulation learning, and predictive accuracy optimization.
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**Quality Control**: Independent validation, benchmark comparison, and comprehensive documentation for institutional learning.
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**Output**: Calibrated simulation with validated accuracy metrics, bias correction reports, continuous improvement systems, and enhanced decision support reliability.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [system-type] | --business-ecosystem | --organizational-dynamics | --market-evolution | --feedback-loops
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description: Model complex system dynamics with feedback loops, delays, and emergent behavior analysis
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---
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# System Dynamics Modeler
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Model complex system dynamics with comprehensive feedback analysis and emergent behavior prediction: **$ARGUMENTS**
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## Current System Context
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- System type: Based on $ARGUMENTS (business ecosystem, organizational dynamics, market evolution, feedback loops)
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- System boundaries: Components, stakeholders, and environmental factors included in the model
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- Key variables: Stock and flow variables, feedback mechanisms, and delay structures
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- Behavior patterns: Current system performance and historical dynamics
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## Task
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Build comprehensive system dynamics model with feedback loops and emergent behavior analysis:
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**System Type**: Use $ARGUMENTS to model business ecosystems, organizational dynamics, market evolution, or feedback loop systems
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**System Dynamics Framework**:
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1. **System Architecture** - Stock and flow identification, causal loop mapping, and boundary definition
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2. **Feedback Structure** - Reinforcing loops, balancing loops, and delay modeling with policy resistance analysis
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3. **Dynamic Simulation** - Time-based behavior analysis, scenario testing, and sensitivity analysis
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4. **Emergent Behavior** - Non-linear effects, unintended consequences, and system archetypes identification
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5. **Policy Testing** - Intervention analysis, leverage point identification, and strategy optimization
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6. **Learning Laboratory** - What-if experimentation, mental model testing, and insight generation
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**Advanced Features**: Nonlinear modeling, stochastic elements, multi-level hierarchy modeling, and behavioral dynamics integration.
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**Strategic Applications**: Policy design, organizational change, strategic planning, and complex problem solving with systems thinking.
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**Output**: Complete system dynamics model with causal structure, simulation results, policy recommendations, and strategic insights for complex system optimization and management.
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---
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allowed-tools: Read, Write, Edit, WebSearch
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argument-hint: [timeline-type] | --product-development | --market-adoption | --business-transformation | --competitive-response
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description: Compress real-world timelines into rapid simulation cycles with accelerated learning and decision optimization
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---
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# Timeline Compressor
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Compress real-world timelines into rapid simulation cycles for exponential learning acceleration: **$ARGUMENTS**
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## Current Timeline Context
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- Timeline type: Based on $ARGUMENTS (product development, market adoption, business transformation, competitive response)
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- Original duration: Real-world timeline length and key phases
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- Compression goals: Decision acceleration, risk exploration, learning speed, or option generation
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- Critical milestones: Key events and dependencies that must be preserved
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## Task
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Implement systematic timeline compression with rapid iteration and decision acceleration:
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**Timeline Type**: Use $ARGUMENTS to compress product development cycles, market adoption patterns, business transformations, or competitive responses
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**Compression Framework**:
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1. **Timeline Architecture** - Temporal structure mapping, dependency analysis, and compressible component identification
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2. **Compression Strategy** - Methodology selection, acceleration factor calibration, and fidelity trade-off optimization
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3. **Rapid Iteration Engine** - Micro, mini, and macro-cycle design with parallel processing capabilities
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4. **Confidence Management** - Uncertainty quantification, risk-adjusted decision making, and validation systems
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5. **Scenario Multiplication** - Exponential scenario exploration with interaction modeling and synthesis
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6. **Decision Integration** - Acceleration optimization, validation frameworks, and strategic momentum creation
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**Advanced Features**: Monte Carlo acceleration, scenario interaction modeling, real-time validation, and adaptive compression ratios.
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**Learning Optimization**: Continuous improvement tracking, model refinement, and knowledge transfer for institutional capability building.
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**Output**: Compressed timeline analysis with acceleration strategies, scenario outcomes, confidence assessments, and implementation roadmaps for exponential learning and decision advantage.
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