212 lines
7.5 KiB
Markdown
212 lines
7.5 KiB
Markdown
# Decision Engine
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## Multi-Criteria Decision Making System
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The Decision Engine provides sophisticated decision-making capabilities that enhance Claude Code's ability to make optimal choices in complex development scenarios.
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### Decision Framework
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#### Decision Types
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```yaml
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decision_categories:
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architectural_decisions:
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- technology_selection: "React vs Angular vs Vue"
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- pattern_choice: "Microservices vs Monolith"
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- integration_approach: "REST vs GraphQL vs gRPC"
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- scalability_strategy: "Horizontal vs Vertical scaling"
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implementation_decisions:
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- algorithm_selection: "Performance vs Memory trade-offs"
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- optimization_approach: "Premature vs Strategic optimization"
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- library_choice: "Build vs Buy vs Open Source"
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- coding_patterns: "Functional vs OOP approaches"
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process_decisions:
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- methodology_adaptation: "Agile practices customization"
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- tool_selection: "Development tool stack"
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- workflow_optimization: "CI/CD pipeline design"
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- team_organization: "Role assignments and responsibilities"
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strategic_decisions:
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- feature_prioritization: "Business value vs Technical debt"
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- resource_allocation: "Team capacity planning"
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- risk_mitigation: "Security vs Speed trade-offs"
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- timeline_adjustment: "Quality vs Delivery speed"
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```
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### Decision Making Process
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#### Multi-Criteria Analysis
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```yaml
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criteria_evaluation:
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criteria_definition:
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technical_criteria:
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- performance_impact: "Response time, throughput"
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- scalability_potential: "Growth accommodation"
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- maintainability_score: "Code complexity, documentation"
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- security_implications: "Vulnerability surface, compliance"
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business_criteria:
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- cost_implications: "Development, operational, maintenance"
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- time_to_market: "Implementation speed"
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- user_value: "Feature impact on user experience"
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- strategic_alignment: "Company vision alignment"
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risk_criteria:
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- implementation_risk: "Technical complexity, unknowns"
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- operational_risk: "Production stability impact"
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- dependency_risk: "Third-party reliability"
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- change_risk: "Future modification difficulty"
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weight_assignment:
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- stakeholder_priorities: "PM, Architect, Business input"
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- project_phase_weights: "Different priorities per phase"
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- historical_success_factors: "What worked before"
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- context_adjustments: "Current project specifics"
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scoring_mechanism:
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- normalized_scores: "0-100 scale for all criteria"
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- weighted_aggregation: "Importance-weighted sum"
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- sensitivity_analysis: "Impact of weight changes"
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- confidence_levels: "Certainty in assessments"
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```
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#### Multi-Persona Consultation
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```yaml
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consultation_process:
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1_identify_stakeholders:
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- relevant_personas: ["architect", "security", "qa", "pm"]
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- expertise_required: "Domain-specific knowledge needed"
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- decision_impact: "Who will be affected"
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2_gather_perspectives:
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consultation_request:
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- decision_context: "Current situation and constraints"
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- options_available: "Alternatives being considered"
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- evaluation_criteria: "How to assess options"
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- time_constraints: "Decision deadline"
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3_synthesize_input:
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- extract_recommendations: "Each persona's preference"
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- identify_agreements: "Consensus areas"
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- resolve_conflicts: "Conflicting recommendations"
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- weight_by_expertise: "Domain expert opinions prioritized"
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4_generate_recommendation:
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- combined_analysis: "Integrated assessment"
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- rationale_documentation: "Why this choice"
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- risk_assessment: "Potential downsides"
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- implementation_guide: "How to execute"
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```
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### Claude Code Integration
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#### Enhanced Decision Making for Development Tasks
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```python
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async def make_technology_decision(requirements, constraints):
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"""
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Use Decision Engine to choose optimal technology stack
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"""
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# Gather technical requirements using Read tool
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project_analysis = await analyze_project_requirements(requirements)
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# Get multi-persona input
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architect_input = await consult_persona("architect", project_analysis)
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security_input = await consult_persona("security", project_analysis)
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performance_input = await consult_persona("qa", project_analysis)
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# Apply decision framework
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decision_matrix = create_decision_matrix([
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architect_input, security_input, performance_input
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])
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# Calculate optimal choice
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optimal_choice = calculate_weighted_decision(decision_matrix, constraints)
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# Document decision using Write tool
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await document_decision(optimal_choice, decision_matrix)
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return optimal_choice
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async def optimize_code_implementation(code_context):
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"""
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Decide on optimal implementation approach
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"""
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# Analyze current code using Read and Grep
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code_analysis = await analyze_code_complexity(code_context)
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# Consider multiple implementation strategies
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strategies = [
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"performance_optimized",
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"maintainability_focused",
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"security_hardened",
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"development_speed"
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]
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# Get expert recommendations
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recommendations = await get_expert_recommendations(strategies, code_analysis)
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# Apply decision criteria
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optimal_strategy = decide_implementation_approach(
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recommendations,
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project_priorities(),
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resource_constraints()
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)
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return optimal_strategy
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```
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### Decision Optimization
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#### Trade-off Analysis
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```yaml
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trade_off_analysis:
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pareto_optimization:
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- identify_objectives: "Performance, Cost, Security, Speed"
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- map_solution_space: "All feasible combinations"
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- find_optimal_frontier: "Best trade-off points"
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- select_balanced_solution: "Stakeholder preference"
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sensitivity_testing:
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- vary_weights: "How robust is the decision?"
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- test_assumptions: "What if requirements change?"
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- identify_robust_options: "Decisions that work in multiple scenarios"
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- document_boundaries: "When to reconsider"
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scenario_planning:
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- best_case_analysis: "Everything goes right"
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- worst_case_analysis: "Murphy's law scenarios"
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- likely_scenario: "Most probable outcome"
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- contingency_planning: "Backup options"
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```
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### Decision Validation and Learning
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#### Outcome Tracking
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```yaml
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decision_tracking:
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implementation_monitoring:
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- measure_actual_outcomes: "Did we achieve objectives?"
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- compare_to_predictions: "Were estimates accurate?"
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- identify_deviations: "What went differently?"
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- extract_lessons: "What did we learn?"
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pattern_development:
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- successful_decisions: "What patterns led to success?"
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- failed_decisions: "What patterns to avoid?"
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- context_factors: "When do patterns apply?"
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- improvement_opportunities: "How to decide better?"
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```
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### Commands for Claude Code
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```bash
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# Decision support commands
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bmad decide --context "api-design" --options "rest,graphql,grpc"
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bmad evaluate --criteria "performance,security,maintainability" --weights "0.4,0.3,0.3"
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bmad tradeoff --analyze "speed-vs-quality" --constraints "timeline=tight"
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bmad recommend --decision-type "architecture" --project-phase "design"
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```
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This Decision Engine transforms Claude Code into an intelligent decision-making partner that can navigate complex technical and business trade-offs with the wisdom of multiple domain experts. |