Keyboard Maestro MCP Server

# TASK_55: km_predictive_automation - Machine Learning-Powered Predictive Automation **Created By**: Agent_3 (ADDER+ Framework) | **Priority**: HIGH | **Duration**: 6-8 hours **Technique Focus**: Machine Learning + Performance Optimization + Predictive Analytics + System Intelligence **Size Constraint**: Target <250 lines/module, Max 400 if splitting awkward ## 🚦 Status & Assignment **Status**: COMPLETED ✅ **Assigned**: Agent_1 (ADDER+ Framework) **Dependencies**: TASK_50 (Analytics Engine), TASK_40 (AI Processing), TASK_49 (Ecosystem Orchestrator) **Blocking**: Advanced Strategic Extensions (TASK_56-68) **Completed**: 2025-07-04 by Agent_1 ## 📖 Required Reading (Complete before starting) - [ ] **TODO.md Status**: ✅ COMPLETED - Verified current task assignments and strategic extension priorities - [ ] **Protocol Compliance**: ✅ COMPLETED - Read FASTMCP_PYTHON_PROTOCOL.md and KM_MCP.md - [ ] **Analytics Engine Integration**: Review TASK_50 implementation for ML insights and performance data - [ ] **AI Processing Integration**: Review TASK_40 implementation for model management and intelligence - [ ] **Ecosystem Orchestrator**: Review TASK_49 implementation for system coordination capabilities - [ ] **Performance Data Sources**: Understanding existing metrics collection and analysis systems ## 🎯 Implementation Analysis **Classification**: Advanced Strategic Extension - Machine Learning and Predictive Analytics **Scope**: Predictive automation system with ML-powered optimization and proactive system management **Integration Points**: Analytics Engine (TASK_50), AI Processing (TASK_40), Ecosystem Orchestrator (TASK_49) <thinking> Predictive Automation System Requirements: 1. Machine Learning Models for pattern recognition and prediction 2. Proactive system optimization based on historical data and trends 3. Intelligent resource allocation and performance prediction 4. Integration with existing analytics and AI systems 5. Real-time prediction and optimization recommendations 6. Performance forecasting and capacity planning 7. Automated optimization triggers and actions </thinking> ## ✅ Implementation Subtasks (Sequential completion with TODO.md integration) ### Phase 1: Setup & Analysis - [ ] **TODO.md Assignment**: ✅ COMPLETED - Task marked IN_PROGRESS and assigned to Agent_3 - [ ] **Protocol Review**: ✅ COMPLETED - FASTMCP and KM protocols understood - [ ] **Analytics Integration Analysis**: Review TASK_50 analytics engine for data sources and ML capabilities - [ ] **AI Processing Analysis**: Review TASK_40 AI processing for model management and intelligence features - [ ] **Ecosystem Coordination**: Review TASK_49 orchestrator for system-wide coordination patterns ### Phase 2: Core Predictive System Implementation - [ ] **Predictive Types System**: Create branded types for PredictiveModel, PredictionRequest, OptimizationSuggestion - [ ] **ML Model Management**: Implement PredictiveModelManager with pattern recognition and forecasting models - [ ] **Performance Predictor**: Create PerformancePredictor for system performance forecasting and capacity planning - [ ] **Optimization Engine**: Implement OptimizationEngine for proactive system optimization recommendations - [ ] **Resource Predictor**: Create ResourcePredictor for intelligent resource allocation and usage forecasting ### Phase 3: Advanced Prediction Features - [ ] **Pattern Recognition**: Implement PatternRecognitionEngine for automation workflow pattern analysis - [ ] **Anomaly Prediction**: Create AnomalyPredictor for proactive issue detection and prevention - [ ] **Capacity Planning**: Implement CapacityPlanner for resource scaling and optimization recommendations - [ ] **Workflow Optimization**: Create WorkflowOptimizer for intelligent automation workflow improvements - [ ] **Predictive Alerts**: Implement PredictiveAlertSystem for proactive notification and action triggering ### Phase 4: Integration & MCP Tools - [ ] **Analytics Integration**: Deep integration with TASK_50 analytics engine for data sources - [ ] **AI Processing Integration**: Integration with TASK_40 AI processing for advanced ML capabilities - [ ] **MCP Tool Implementation**: Create km_predictive_automation tool with comprehensive operations - [ ] **Prediction API**: Implement prediction request processing with security validation - [ ] **Optimization API**: Create optimization recommendation system with action automation ### Phase 5: Testing & Validation - [ ] **Property-Based Testing**: Comprehensive Hypothesis-driven validation of prediction algorithms - [ ] **ML Model Testing**: Validation of prediction accuracy, performance, and reliability - [ ] **Integration Testing**: Cross-component validation with analytics and AI processing systems - [ ] **Performance Testing**: Response time validation, prediction accuracy, optimization effectiveness - [ ] **Security Testing**: Input validation, model security, prediction integrity ### Phase 6: Advanced Features & Optimization - [ ] **Real-Time Prediction**: Implement real-time prediction capabilities with streaming data processing - [ ] **Automated Optimization**: Create automated optimization actions based on predictions - [ ] **Learning Adaptation**: Implement adaptive learning for continuous model improvement - [ ] **Predictive Dashboards**: Create visual dashboards for prediction insights and optimization status - [ ] **System Health Prediction**: Advanced system health forecasting and maintenance scheduling ### Phase 7: Completion & Handoff (MANDATORY) - [ ] **Quality Verification**: Verify all prediction algorithms and optimization features working correctly - [ ] **Final Testing**: Ensure all tests passing and TESTING.md current with predictive automation status - [ ] **TASK_55.md Completion**: Mark all subtasks complete with final implementation status - [ ] **TODO.md Completion Update**: Update task status to COMPLETE with completion timestamp - [ ] **Next Task Assignment**: Update TODO.md with next strategic extension task assignment ## 🔧 Implementation Files & Specifications ### Core Predictive System ``` src/prediction/ ├── __init__.py # Predictive automation module initialization ├── predictive_types.py # Branded types: PredictiveModel, PredictionRequest, OptimizationSuggestion ├── model_manager.py # ML model management and pattern recognition ├── performance_predictor.py # System performance forecasting and capacity planning ├── optimization_engine.py # Proactive optimization recommendations and automation ├── resource_predictor.py # Resource allocation and usage forecasting ├── pattern_recognition.py # Automation workflow pattern analysis ├── anomaly_predictor.py # Proactive issue detection and prevention ├── capacity_planner.py # Resource scaling and optimization recommendations ├── workflow_optimizer.py # Intelligent automation workflow improvements └── predictive_alerts.py # Proactive notification and action triggering ``` ### Server Tools Integration ``` src/server/tools/ ├── predictive_automation_tools.py # MCP tools for predictive automation operations └── prediction_analytics_tools.py # Integration tools for analytics and AI processing ``` ### Test Suite ``` tests/prediction/ ├── test_predictive_types.py # Branded types and data structure testing ├── test_model_manager.py # ML model management testing ├── test_performance_predictor.py # Performance forecasting testing ├── test_optimization_engine.py # Optimization recommendation testing ├── test_resource_predictor.py # Resource prediction testing ├── test_pattern_recognition.py # Pattern analysis testing ├── test_anomaly_predictor.py # Anomaly detection testing ├── test_capacity_planner.py # Capacity planning testing ├── test_workflow_optimizer.py # Workflow optimization testing ├── test_predictive_alerts.py # Alert system testing └── test_prediction_integration.py # Cross-component integration testing ``` ## 🏗️ Modularity Strategy ### Core Architecture - **PredictiveModelManager**: Central ML model coordination (<250 lines) - **PerformancePredictor**: System performance forecasting (<200 lines) - **OptimizationEngine**: Proactive optimization recommendations (<300 lines) - **ResourcePredictor**: Resource allocation and usage prediction (<250 lines) - **PatternRecognitionEngine**: Workflow pattern analysis (<350 lines) ### Integration Strategy - **Analytics Integration**: Deep integration with TASK_50 for historical data and trend analysis - **AI Processing Integration**: Leverage TASK_40 ML capabilities for advanced prediction models - **Ecosystem Coordination**: Integration with TASK_49 for system-wide optimization orchestration - **Real-Time Processing**: Streaming data processing for continuous prediction and optimization ### Security & Performance - **Model Security**: Secure ML model storage and access with encryption and validation - **Prediction Integrity**: Cryptographic validation of prediction results and recommendations - **Performance Optimization**: <1s predictions, <2s optimization recommendations, <100ms real-time alerts - **Resource Management**: Efficient model loading, prediction caching, and memory optimization ## ✅ Success Criteria ### Functional Requirements - Complete predictive automation system with ML-powered pattern recognition and optimization - Integration with analytics engine (TASK_50) for historical data analysis and trend forecasting - Integration with AI processing (TASK_40) for advanced ML model management and intelligence - Real-time prediction capabilities with proactive optimization recommendations - Automated optimization actions based on prediction results and system analysis - Comprehensive test suite with property-based testing and ML model validation ### Performance Requirements - Prediction generation: <1s for standard predictions, <5s for complex analysis - Optimization recommendations: <2s generation time, <10s for complex system analysis - Real-time alerts: <100ms processing time for critical system events - Model accuracy: >85% prediction accuracy for performance and resource forecasting - System impact: <5% overhead on existing system performance ### Integration Requirements - Deep integration with analytics engine for data sources and trend analysis - Seamless integration with AI processing for ML model management - Cross-component coordination with ecosystem orchestrator for system-wide optimization - MCP tool registration with comprehensive prediction and optimization operations - Compatible with existing security framework and audit logging requirements ### Quality Requirements - Complete ADDER+ technique implementation (contracts, defensive programming, type safety, property testing) - Comprehensive error handling with graceful degradation and recovery strategies - Security validation with input sanitization and model integrity protection - Performance monitoring with optimization effectiveness tracking and continuous improvement - Documentation updates with architectural decisions and integration patterns **Execute with systematic precision, complete ML technique integration, intelligent prediction algorithms, comprehensive system optimization, and transparent analytics coordination.**