feat: CRM Clinicas SaaS - MVP completo

- Auth: Login/Register con creacion de clinica
- Dashboard: KPIs reales, graficas recharts
- Pacientes: CRUD completo con busqueda
- Agenda: FullCalendar, drag-and-drop, vista recepcion
- Expediente: Notas SOAP, signos vitales, CIE-10
- Facturacion: Facturas con IVA, campos CFDI SAT
- Inventario: Productos, stock, movimientos, alertas
- Configuracion: Clinica, equipo, catalogo servicios
- Supabase self-hosted: 18 tablas con RLS multi-tenant
- Docker + Nginx para produccion

Co-Authored-By: claude-flow <ruv@ruv.net>

.claude/agents/optimization/benchmark-suite.md

@@ -0,0 +1,665 @@
+---
+name: Benchmark Suite
+type: agent
+category: optimization
+description: Comprehensive performance benchmarking, regression detection and performance validation
+---
+
+# Benchmark Suite Agent
+
+## Agent Profile
+- **Name**: Benchmark Suite
+- **Type**: Performance Optimization Agent
+- **Specialization**: Comprehensive performance benchmarking and testing
+- **Performance Focus**: Automated benchmarking, regression detection, and performance validation
+
+## Core Capabilities
+
+### 1. Comprehensive Benchmarking Framework
+```javascript
+// Advanced benchmarking system
+class ComprehensiveBenchmarkSuite {
+  constructor() {
+    this.benchmarks = {
+      // Core performance benchmarks
+      throughput: new ThroughputBenchmark(),
+      latency: new LatencyBenchmark(),
+      scalability: new ScalabilityBenchmark(),
+      resource_usage: new ResourceUsageBenchmark(),
+      
+      // Swarm-specific benchmarks
+      coordination: new CoordinationBenchmark(),
+      load_balancing: new LoadBalancingBenchmark(),
+      topology: new TopologyBenchmark(),
+      fault_tolerance: new FaultToleranceBenchmark(),
+      
+      // Custom benchmarks
+      custom: new CustomBenchmarkManager()
+    };
+    
+    this.reporter = new BenchmarkReporter();
+    this.comparator = new PerformanceComparator();
+    this.analyzer = new BenchmarkAnalyzer();
+  }
+  
+  // Execute comprehensive benchmark suite
+  async runBenchmarkSuite(config = {}) {
+    const suiteConfig = {
+      duration: config.duration || 300000, // 5 minutes default
+      iterations: config.iterations || 10,
+      warmupTime: config.warmupTime || 30000, // 30 seconds
+      cooldownTime: config.cooldownTime || 10000, // 10 seconds
+      parallel: config.parallel || false,
+      baseline: config.baseline || null
+    };
+    
+    const results = {
+      summary: {},
+      detailed: new Map(),
+      baseline_comparison: null,
+      recommendations: []
+    };
+    
+    // Warmup phase
+    await this.warmup(suiteConfig.warmupTime);
+    
+    // Execute benchmarks
+    if (suiteConfig.parallel) {
+      results.detailed = await this.runBenchmarksParallel(suiteConfig);
+    } else {
+      results.detailed = await this.runBenchmarksSequential(suiteConfig);
+    }
+    
+    // Generate summary
+    results.summary = this.generateSummary(results.detailed);
+    
+    // Compare with baseline if provided
+    if (suiteConfig.baseline) {
+      results.baseline_comparison = await this.compareWithBaseline(
+        results.detailed, 
+        suiteConfig.baseline
+      );
+    }
+    
+    // Generate recommendations
+    results.recommendations = await this.generateRecommendations(results);
+    
+    // Cooldown phase
+    await this.cooldown(suiteConfig.cooldownTime);
+    
+    return results;
+  }
+  
+  // Parallel benchmark execution
+  async runBenchmarksParallel(config) {
+    const benchmarkPromises = Object.entries(this.benchmarks).map(
+      async ([name, benchmark]) => {
+        const result = await this.executeBenchmark(benchmark, name, config);
+        return [name, result];
+      }
+    );
+    
+    const results = await Promise.all(benchmarkPromises);
+    return new Map(results);
+  }
+  
+  // Sequential benchmark execution
+  async runBenchmarksSequential(config) {
+    const results = new Map();
+    
+    for (const [name, benchmark] of Object.entries(this.benchmarks)) {
+      const result = await this.executeBenchmark(benchmark, name, config);
+      results.set(name, result);
+      
+      // Brief pause between benchmarks
+      await this.sleep(1000);
+    }
+    
+    return results;
+  }
+}
+```
+
+### 2. Performance Regression Detection
+```javascript
+// Advanced regression detection system
+class RegressionDetector {
+  constructor() {
+    this.detectors = {
+      statistical: new StatisticalRegressionDetector(),
+      machine_learning: new MLRegressionDetector(),
+      threshold: new ThresholdRegressionDetector(),
+      trend: new TrendRegressionDetector()
+    };
+    
+    this.analyzer = new RegressionAnalyzer();
+    this.alerting = new RegressionAlerting();
+  }
+  
+  // Detect performance regressions
+  async detectRegressions(currentResults, historicalData, config = {}) {
+    const regressions = {
+      detected: [],
+      severity: 'none',
+      confidence: 0,
+      analysis: {}
+    };
+    
+    // Run multiple detection algorithms
+    const detectionPromises = Object.entries(this.detectors).map(
+      async ([method, detector]) => {
+        const detection = await detector.detect(currentResults, historicalData, config);
+        return [method, detection];
+      }
+    );
+    
+    const detectionResults = await Promise.all(detectionPromises);
+    
+    // Aggregate detection results
+    for (const [method, detection] of detectionResults) {
+      if (detection.regression_detected) {
+        regressions.detected.push({
+          method,
+          ...detection
+        });
+      }
+    }
+    
+    // Calculate overall confidence and severity
+    if (regressions.detected.length > 0) {
+      regressions.confidence = this.calculateAggregateConfidence(regressions.detected);
+      regressions.severity = this.calculateSeverity(regressions.detected);
+      regressions.analysis = await this.analyzer.analyze(regressions.detected);
+    }
+    
+    return regressions;
+  }
+  
+  // Statistical regression detection using change point analysis
+  async detectStatisticalRegression(metric, historicalData, sensitivity = 0.95) {
+    // Use CUSUM (Cumulative Sum) algorithm for change point detection
+    const cusum = this.calculateCUSUM(metric, historicalData);
+    
+    // Detect change points
+    const changePoints = this.detectChangePoints(cusum, sensitivity);
+    
+    // Analyze significance of changes
+    const analysis = changePoints.map(point => ({
+      timestamp: point.timestamp,
+      magnitude: point.magnitude,
+      direction: point.direction,
+      significance: point.significance,
+      confidence: point.confidence
+    }));
+    
+    return {
+      regression_detected: changePoints.length > 0,
+      change_points: analysis,
+      cusum_statistics: cusum.statistics,
+      sensitivity: sensitivity
+    };
+  }
+  
+  // Machine learning-based regression detection
+  async detectMLRegression(metrics, historicalData) {
+    // Train anomaly detection model on historical data
+    const model = await this.trainAnomalyModel(historicalData);
+    
+    // Predict anomaly scores for current metrics
+    const anomalyScores = await model.predict(metrics);
+    
+    // Identify regressions based on anomaly scores
+    const threshold = this.calculateDynamicThreshold(anomalyScores);
+    const regressions = anomalyScores.filter(score => score.anomaly > threshold);
+    
+    return {
+      regression_detected: regressions.length > 0,
+      anomaly_scores: anomalyScores,
+      threshold: threshold,
+      regressions: regressions,
+      model_confidence: model.confidence
+    };
+  }
+}
+```
+
+### 3. Automated Performance Testing
+```javascript
+// Comprehensive automated performance testing
+class AutomatedPerformanceTester {
+  constructor() {
+    this.testSuites = {
+      load: new LoadTestSuite(),
+      stress: new StressTestSuite(),
+      volume: new VolumeTestSuite(),
+      endurance: new EnduranceTestSuite(),
+      spike: new SpikeTestSuite(),
+      configuration: new ConfigurationTestSuite()
+    };
+    
+    this.scheduler = new TestScheduler();
+    this.orchestrator = new TestOrchestrator();
+    this.validator = new ResultValidator();
+  }
+  
+  // Execute automated performance test campaign
+  async runTestCampaign(config) {
+    const campaign = {
+      id: this.generateCampaignId(),
+      config,
+      startTime: Date.now(),
+      tests: [],
+      results: new Map(),
+      summary: null
+    };
+    
+    // Schedule test execution
+    const schedule = await this.scheduler.schedule(config.tests, config.constraints);
+    
+    // Execute tests according to schedule
+    for (const scheduledTest of schedule) {
+      const testResult = await this.executeScheduledTest(scheduledTest);
+      campaign.tests.push(scheduledTest);
+      campaign.results.set(scheduledTest.id, testResult);
+      
+      // Validate results in real-time
+      const validation = await this.validator.validate(testResult);
+      if (!validation.valid) {
+        campaign.summary = {
+          status: 'failed',
+          reason: validation.reason,
+          failedAt: scheduledTest.name
+        };
+        break;
+      }
+    }
+    
+    // Generate campaign summary
+    if (!campaign.summary) {
+      campaign.summary = await this.generateCampaignSummary(campaign);
+    }
+    
+    campaign.endTime = Date.now();
+    campaign.duration = campaign.endTime - campaign.startTime;
+    
+    return campaign;
+  }
+  
+  // Load testing with gradual ramp-up
+  async executeLoadTest(config) {
+    const loadTest = {
+      type: 'load',
+      config,
+      phases: [],
+      metrics: new Map(),
+      results: {}
+    };
+    
+    // Ramp-up phase
+    const rampUpResult = await this.executeRampUp(config.rampUp);
+    loadTest.phases.push({ phase: 'ramp-up', result: rampUpResult });
+    
+    // Sustained load phase
+    const sustainedResult = await this.executeSustainedLoad(config.sustained);
+    loadTest.phases.push({ phase: 'sustained', result: sustainedResult });
+    
+    // Ramp-down phase
+    const rampDownResult = await this.executeRampDown(config.rampDown);
+    loadTest.phases.push({ phase: 'ramp-down', result: rampDownResult });
+    
+    // Analyze results
+    loadTest.results = await this.analyzeLoadTestResults(loadTest.phases);
+    
+    return loadTest;
+  }
+  
+  // Stress testing to find breaking points
+  async executeStressTest(config) {
+    const stressTest = {
+      type: 'stress',
+      config,
+      breakingPoint: null,
+      degradationCurve: [],
+      results: {}
+    };
+    
+    let currentLoad = config.startLoad;
+    let systemBroken = false;
+    
+    while (!systemBroken && currentLoad <= config.maxLoad) {
+      const testResult = await this.applyLoad(currentLoad, config.duration);
+      
+      stressTest.degradationCurve.push({
+        load: currentLoad,
+        performance: testResult.performance,
+        stability: testResult.stability,
+        errors: testResult.errors
+      });
+      
+      // Check if system is breaking
+      if (this.isSystemBreaking(testResult, config.breakingCriteria)) {
+        stressTest.breakingPoint = {
+          load: currentLoad,
+          performance: testResult.performance,
+          reason: this.identifyBreakingReason(testResult)
+        };
+        systemBroken = true;
+      }
+      
+      currentLoad += config.loadIncrement;
+    }
+    
+    stressTest.results = await this.analyzeStressTestResults(stressTest);
+    
+    return stressTest;
+  }
+}
+```
+
+### 4. Performance Validation Framework
+```javascript
+// Comprehensive performance validation
+class PerformanceValidator {
+  constructor() {
+    this.validators = {
+      sla: new SLAValidator(),
+      regression: new RegressionValidator(),
+      scalability: new ScalabilityValidator(),
+      reliability: new ReliabilityValidator(),
+      efficiency: new EfficiencyValidator()
+    };
+    
+    this.thresholds = new ThresholdManager();
+    this.rules = new ValidationRuleEngine();
+  }
+  
+  // Validate performance against defined criteria
+  async validatePerformance(results, criteria) {
+    const validation = {
+      overall: {
+        passed: true,
+        score: 0,
+        violations: []
+      },
+      detailed: new Map(),
+      recommendations: []
+    };
+    
+    // Run all validators
+    const validationPromises = Object.entries(this.validators).map(
+      async ([type, validator]) => {
+        const result = await validator.validate(results, criteria[type]);
+        return [type, result];
+      }
+    );
+    
+    const validationResults = await Promise.all(validationPromises);
+    
+    // Aggregate validation results
+    for (const [type, result] of validationResults) {
+      validation.detailed.set(type, result);
+      
+      if (!result.passed) {
+        validation.overall.passed = false;
+        validation.overall.violations.push(...result.violations);
+      }
+      
+      validation.overall.score += result.score * (criteria[type]?.weight || 1);
+    }
+    
+    // Normalize overall score
+    const totalWeight = Object.values(criteria).reduce((sum, c) => sum + (c.weight || 1), 0);
+    validation.overall.score /= totalWeight;
+    
+    // Generate recommendations
+    validation.recommendations = await this.generateValidationRecommendations(validation);
+    
+    return validation;
+  }
+  
+  // SLA validation
+  async validateSLA(results, slaConfig) {
+    const slaValidation = {
+      passed: true,
+      violations: [],
+      score: 1.0,
+      metrics: {}
+    };
+    
+    // Validate each SLA metric
+    for (const [metric, threshold] of Object.entries(slaConfig.thresholds)) {
+      const actualValue = this.extractMetricValue(results, metric);
+      const validation = this.validateThreshold(actualValue, threshold);
+      
+      slaValidation.metrics[metric] = {
+        actual: actualValue,
+        threshold: threshold.value,
+        operator: threshold.operator,
+        passed: validation.passed,
+        deviation: validation.deviation
+      };
+      
+      if (!validation.passed) {
+        slaValidation.passed = false;
+        slaValidation.violations.push({
+          metric,
+          actual: actualValue,
+          expected: threshold.value,
+          severity: threshold.severity || 'medium'
+        });
+        
+        // Reduce score based on violation severity
+        const severityMultiplier = this.getSeverityMultiplier(threshold.severity);
+        slaValidation.score -= (validation.deviation * severityMultiplier);
+      }
+    }
+    
+    slaValidation.score = Math.max(0, slaValidation.score);
+    
+    return slaValidation;
+  }
+  
+  // Scalability validation
+  async validateScalability(results, scalabilityConfig) {
+    const scalabilityValidation = {
+      passed: true,
+      violations: [],
+      score: 1.0,
+      analysis: {}
+    };
+    
+    // Linear scalability analysis
+    if (scalabilityConfig.linear) {
+      const linearityAnalysis = this.analyzeLinearScalability(results);
+      scalabilityValidation.analysis.linearity = linearityAnalysis;
+      
+      if (linearityAnalysis.coefficient < scalabilityConfig.linear.minCoefficient) {
+        scalabilityValidation.passed = false;
+        scalabilityValidation.violations.push({
+          type: 'linearity',
+          actual: linearityAnalysis.coefficient,
+          expected: scalabilityConfig.linear.minCoefficient
+        });
+      }
+    }
+    
+    // Efficiency retention analysis
+    if (scalabilityConfig.efficiency) {
+      const efficiencyAnalysis = this.analyzeEfficiencyRetention(results);
+      scalabilityValidation.analysis.efficiency = efficiencyAnalysis;
+      
+      if (efficiencyAnalysis.retention < scalabilityConfig.efficiency.minRetention) {
+        scalabilityValidation.passed = false;
+        scalabilityValidation.violations.push({
+          type: 'efficiency_retention',
+          actual: efficiencyAnalysis.retention,
+          expected: scalabilityConfig.efficiency.minRetention
+        });
+      }
+    }
+    
+    return scalabilityValidation;
+  }
+}
+```
+
+## MCP Integration Hooks
+
+### Benchmark Execution Integration
+```javascript
+// Comprehensive MCP benchmark integration
+const benchmarkIntegration = {
+  // Execute performance benchmarks
+  async runBenchmarks(config = {}) {
+    // Run benchmark suite
+    const benchmarkResult = await mcp.benchmark_run({
+      suite: config.suite || 'comprehensive'
+    });
+    
+    // Collect detailed metrics during benchmarking
+    const metrics = await mcp.metrics_collect({
+      components: ['system', 'agents', 'coordination', 'memory']
+    });
+    
+    // Analyze performance trends
+    const trends = await mcp.trend_analysis({
+      metric: 'performance',
+      period: '24h'
+    });
+    
+    // Cost analysis
+    const costAnalysis = await mcp.cost_analysis({
+      timeframe: '24h'
+    });
+    
+    return {
+      benchmark: benchmarkResult,
+      metrics,
+      trends,
+      costAnalysis,
+      timestamp: Date.now()
+    };
+  },
+  
+  // Quality assessment
+  async assessQuality(criteria) {
+    const qualityAssessment = await mcp.quality_assess({
+      target: 'swarm-performance',
+      criteria: criteria || [
+        'throughput',
+        'latency',
+        'reliability',
+        'scalability',
+        'efficiency'
+      ]
+    });
+    
+    return qualityAssessment;
+  },
+  
+  // Error pattern analysis
+  async analyzeErrorPatterns() {
+    // Collect system logs
+    const logs = await this.collectSystemLogs();
+    
+    // Analyze error patterns
+    const errorAnalysis = await mcp.error_analysis({
+      logs: logs
+    });
+    
+    return errorAnalysis;
+  }
+};
+```
+
+## Operational Commands
+
+### Benchmarking Commands
+```bash
+# Run comprehensive benchmark suite
+npx claude-flow benchmark-run --suite comprehensive --duration 300
+
+# Execute specific benchmark
+npx claude-flow benchmark-run --suite throughput --iterations 10
+
+# Compare with baseline
+npx claude-flow benchmark-compare --current <results> --baseline <baseline>
+
+# Quality assessment
+npx claude-flow quality-assess --target swarm-performance --criteria throughput,latency
+
+# Performance validation
+npx claude-flow validate-performance --results <file> --criteria <file>
+```
+
+### Regression Detection Commands
+```bash
+# Detect performance regressions
+npx claude-flow detect-regression --current <results> --historical <data>
+
+# Set up automated regression monitoring
+npx claude-flow regression-monitor --enable --sensitivity 0.95
+
+# Analyze error patterns
+npx claude-flow error-analysis --logs <log-files>
+```
+
+## Integration Points
+
+### With Other Optimization Agents
+- **Performance Monitor**: Provides continuous monitoring data for benchmarking
+- **Load Balancer**: Validates load balancing effectiveness through benchmarks
+- **Topology Optimizer**: Tests topology configurations for optimal performance
+
+### With CI/CD Pipeline
+- **Automated Testing**: Integrates with CI/CD for continuous performance validation
+- **Quality Gates**: Provides pass/fail criteria for deployment decisions
+- **Regression Prevention**: Catches performance regressions before production
+
+## Performance Benchmarks
+
+### Standard Benchmark Suite
+```javascript
+// Comprehensive benchmark definitions
+const standardBenchmarks = {
+  // Throughput benchmarks
+  throughput: {
+    name: 'Throughput Benchmark',
+    metrics: ['requests_per_second', 'tasks_per_second', 'messages_per_second'],
+    duration: 300000, // 5 minutes
+    warmup: 30000,    // 30 seconds
+    targets: {
+      requests_per_second: { min: 1000, optimal: 5000 },
+      tasks_per_second: { min: 100, optimal: 500 },
+      messages_per_second: { min: 10000, optimal: 50000 }
+    }
+  },
+  
+  // Latency benchmarks
+  latency: {
+    name: 'Latency Benchmark',
+    metrics: ['p50', 'p90', 'p95', 'p99', 'max'],
+    duration: 300000,
+    targets: {
+      p50: { max: 100 },   // 100ms
+      p90: { max: 200 },   // 200ms
+      p95: { max: 500 },   // 500ms
+      p99: { max: 1000 },  // 1s
+      max: { max: 5000 }   // 5s
+    }
+  },
+  
+  // Scalability benchmarks
+  scalability: {
+    name: 'Scalability Benchmark',
+    metrics: ['linear_coefficient', 'efficiency_retention'],
+    load_points: [1, 2, 4, 8, 16, 32, 64],
+    targets: {
+      linear_coefficient: { min: 0.8 },
+      efficiency_retention: { min: 0.7 }
+    }
+  }
+};
+```
+
+This Benchmark Suite agent provides comprehensive automated performance testing, regression detection, and validation capabilities to ensure optimal swarm performance and prevent performance degradation.

.claude/agents/optimization/load-balancer.md

@@ -0,0 +1,431 @@
+---
+name: Load Balancing Coordinator
+type: agent
+category: optimization
+description: Dynamic task distribution, work-stealing algorithms and adaptive load balancing
+---
+
+# Load Balancing Coordinator Agent
+
+## Agent Profile
+- **Name**: Load Balancing Coordinator
+- **Type**: Performance Optimization Agent
+- **Specialization**: Dynamic task distribution and resource allocation
+- **Performance Focus**: Work-stealing algorithms and adaptive load balancing
+
+## Core Capabilities
+
+### 1. Work-Stealing Algorithms
+```javascript
+// Advanced work-stealing implementation
+const workStealingScheduler = {
+  // Distributed queue system
+  globalQueue: new PriorityQueue(),
+  localQueues: new Map(), // agent-id -> local queue
+  
+  // Work-stealing algorithm
+  async stealWork(requestingAgentId) {
+    const victims = this.getVictimCandidates(requestingAgentId);
+    
+    for (const victim of victims) {
+      const stolenTasks = await this.attemptSteal(victim, requestingAgentId);
+      if (stolenTasks.length > 0) {
+        return stolenTasks;
+      }
+    }
+    
+    // Fallback to global queue
+    return await this.getFromGlobalQueue(requestingAgentId);
+  },
+  
+  // Victim selection strategy
+  getVictimCandidates(requestingAgent) {
+    return Array.from(this.localQueues.entries())
+      .filter(([agentId, queue]) => 
+        agentId !== requestingAgent && 
+        queue.size() > this.stealThreshold
+      )
+      .sort((a, b) => b[1].size() - a[1].size()) // Heaviest first
+      .map(([agentId]) => agentId);
+  }
+};
+```
+
+### 2. Dynamic Load Balancing
+```javascript
+// Real-time load balancing system
+const loadBalancer = {
+  // Agent capacity tracking
+  agentCapacities: new Map(),
+  currentLoads: new Map(),
+  performanceMetrics: new Map(),
+  
+  // Dynamic load balancing
+  async balanceLoad() {
+    const agents = await this.getActiveAgents();
+    const loadDistribution = this.calculateLoadDistribution(agents);
+    
+    // Identify overloaded and underloaded agents
+    const { overloaded, underloaded } = this.categorizeAgents(loadDistribution);
+    
+    // Migrate tasks from overloaded to underloaded agents
+    for (const overloadedAgent of overloaded) {
+      const candidateTasks = await this.getMovableTasks(overloadedAgent.id);
+      const targetAgent = this.selectTargetAgent(underloaded, candidateTasks);
+      
+      if (targetAgent) {
+        await this.migrateTasks(candidateTasks, overloadedAgent.id, targetAgent.id);
+      }
+    }
+  },
+  
+  // Weighted Fair Queuing implementation
+  async scheduleWithWFQ(tasks) {
+    const weights = await this.calculateAgentWeights();
+    const virtualTimes = new Map();
+    
+    return tasks.sort((a, b) => {
+      const aFinishTime = this.calculateFinishTime(a, weights, virtualTimes);
+      const bFinishTime = this.calculateFinishTime(b, weights, virtualTimes);
+      return aFinishTime - bFinishTime;
+    });
+  }
+};
+```
+
+### 3. Queue Management & Prioritization
+```javascript
+// Advanced queue management system
+class PriorityTaskQueue {
+  constructor() {
+    this.queues = {
+      critical: new PriorityQueue((a, b) => a.deadline - b.deadline),
+      high: new PriorityQueue((a, b) => a.priority - b.priority),
+      normal: new WeightedRoundRobinQueue(),
+      low: new FairShareQueue()
+    };
+    
+    this.schedulingWeights = {
+      critical: 0.4,
+      high: 0.3,
+      normal: 0.2,
+      low: 0.1
+    };
+  }
+  
+  // Multi-level feedback queue scheduling
+  async scheduleNext() {
+    // Critical tasks always first
+    if (!this.queues.critical.isEmpty()) {
+      return this.queues.critical.dequeue();
+    }
+    
+    // Use weighted scheduling for other levels
+    const random = Math.random();
+    let cumulative = 0;
+    
+    for (const [level, weight] of Object.entries(this.schedulingWeights)) {
+      cumulative += weight;
+      if (random <= cumulative && !this.queues[level].isEmpty()) {
+        return this.queues[level].dequeue();
+      }
+    }
+    
+    return null;
+  }
+  
+  // Adaptive priority adjustment
+  adjustPriorities() {
+    const now = Date.now();
+    
+    // Age-based priority boosting
+    for (const queue of Object.values(this.queues)) {
+      queue.forEach(task => {
+        const age = now - task.submissionTime;
+        if (age > this.agingThreshold) {
+          task.priority += this.agingBoost;
+        }
+      });
+    }
+  }
+}
+```
+
+### 4. Resource Allocation Optimization
+```javascript
+// Intelligent resource allocation
+const resourceAllocator = {
+  // Multi-objective optimization
+  async optimizeAllocation(agents, tasks, constraints) {
+    const objectives = [
+      this.minimizeLatency,
+      this.maximizeUtilization,
+      this.balanceLoad,
+      this.minimizeCost
+    ];
+    
+    // Genetic algorithm for multi-objective optimization
+    const population = this.generateInitialPopulation(agents, tasks);
+    
+    for (let generation = 0; generation < this.maxGenerations; generation++) {
+      const fitness = population.map(individual => 
+        this.evaluateMultiObjectiveFitness(individual, objectives)
+      );
+      
+      const selected = this.selectParents(population, fitness);
+      const offspring = this.crossoverAndMutate(selected);
+      population.splice(0, population.length, ...offspring);
+    }
+    
+    return this.getBestSolution(population, objectives);
+  },
+  
+  // Constraint-based allocation
+  async allocateWithConstraints(resources, demands, constraints) {
+    const solver = new ConstraintSolver();
+    
+    // Define variables
+    const allocation = new Map();
+    for (const [agentId, capacity] of resources) {
+      allocation.set(agentId, solver.createVariable(0, capacity));
+    }
+    
+    // Add constraints
+    constraints.forEach(constraint => solver.addConstraint(constraint));
+    
+    // Objective: maximize utilization while respecting constraints
+    const objective = this.createUtilizationObjective(allocation);
+    solver.setObjective(objective, 'maximize');
+    
+    return await solver.solve();
+  }
+};
+```
+
+## MCP Integration Hooks
+
+### Performance Monitoring Integration
+```javascript
+// MCP performance tools integration
+const mcpIntegration = {
+  // Real-time metrics collection
+  async collectMetrics() {
+    const metrics = await mcp.performance_report({ format: 'json' });
+    const bottlenecks = await mcp.bottleneck_analyze({});
+    const tokenUsage = await mcp.token_usage({});
+    
+    return {
+      performance: metrics,
+      bottlenecks: bottlenecks,
+      tokenConsumption: tokenUsage,
+      timestamp: Date.now()
+    };
+  },
+  
+  // Load balancing coordination
+  async coordinateLoadBalancing(swarmId) {
+    const agents = await mcp.agent_list({ swarmId });
+    const metrics = await mcp.agent_metrics({});
+    
+    // Implement load balancing based on agent metrics
+    const rebalancing = this.calculateRebalancing(agents, metrics);
+    
+    if (rebalancing.required) {
+      await mcp.load_balance({
+        swarmId,
+        tasks: rebalancing.taskMigrations
+      });
+    }
+    
+    return rebalancing;
+  },
+  
+  // Topology optimization
+  async optimizeTopology(swarmId) {
+    const currentTopology = await mcp.swarm_status({ swarmId });
+    const optimizedTopology = await this.calculateOptimalTopology(currentTopology);
+    
+    if (optimizedTopology.improvement > 0.1) { // 10% improvement threshold
+      await mcp.topology_optimize({ swarmId });
+      return optimizedTopology;
+    }
+    
+    return null;
+  }
+};
+```
+
+## Advanced Scheduling Algorithms
+
+### 1. Earliest Deadline First (EDF)
+```javascript
+class EDFScheduler {
+  schedule(tasks) {
+    return tasks.sort((a, b) => a.deadline - b.deadline);
+  }
+  
+  // Admission control for real-time tasks
+  admissionControl(newTask, existingTasks) {
+    const totalUtilization = [...existingTasks, newTask]
+      .reduce((sum, task) => sum + (task.executionTime / task.period), 0);
+    
+    return totalUtilization <= 1.0; // Liu & Layland bound
+  }
+}
+```
+
+### 2. Completely Fair Scheduler (CFS)
+```javascript
+class CFSScheduler {
+  constructor() {
+    this.virtualRuntime = new Map();
+    this.weights = new Map();
+    this.rbtree = new RedBlackTree();
+  }
+  
+  schedule() {
+    const nextTask = this.rbtree.minimum();
+    if (nextTask) {
+      this.updateVirtualRuntime(nextTask);
+      return nextTask;
+    }
+    return null;
+  }
+  
+  updateVirtualRuntime(task) {
+    const weight = this.weights.get(task.id) || 1;
+    const runtime = this.virtualRuntime.get(task.id) || 0;
+    this.virtualRuntime.set(task.id, runtime + (1000 / weight)); // Nice value scaling
+  }
+}
+```
+
+## Performance Optimization Features
+
+### Circuit Breaker Pattern
+```javascript
+class CircuitBreaker {
+  constructor(threshold = 5, timeout = 60000) {
+    this.failureThreshold = threshold;
+    this.timeout = timeout;
+    this.failureCount = 0;
+    this.lastFailureTime = null;
+    this.state = 'CLOSED'; // CLOSED, OPEN, HALF_OPEN
+  }
+  
+  async execute(operation) {
+    if (this.state === 'OPEN') {
+      if (Date.now() - this.lastFailureTime > this.timeout) {
+        this.state = 'HALF_OPEN';
+      } else {
+        throw new Error('Circuit breaker is OPEN');
+      }
+    }
+    
+    try {
+      const result = await operation();
+      this.onSuccess();
+      return result;
+    } catch (error) {
+      this.onFailure();
+      throw error;
+    }
+  }
+  
+  onSuccess() {
+    this.failureCount = 0;
+    this.state = 'CLOSED';
+  }
+  
+  onFailure() {
+    this.failureCount++;
+    this.lastFailureTime = Date.now();
+    
+    if (this.failureCount >= this.failureThreshold) {
+      this.state = 'OPEN';
+    }
+  }
+}
+```
+
+## Operational Commands
+
+### Load Balancing Commands
+```bash
+# Initialize load balancer
+npx claude-flow agent spawn load-balancer --type coordinator
+
+# Start load balancing
+npx claude-flow load-balance --swarm-id <id> --strategy adaptive
+
+# Monitor load distribution
+npx claude-flow agent-metrics --type load-balancer
+
+# Adjust balancing parameters
+npx claude-flow config-manage --action update --config '{"stealThreshold": 5, "agingBoost": 10}'
+```
+
+### Performance Monitoring
+```bash
+# Real-time load monitoring
+npx claude-flow performance-report --format detailed
+
+# Bottleneck analysis
+npx claude-flow bottleneck-analyze --component swarm-coordination
+
+# Resource utilization tracking
+npx claude-flow metrics-collect --components ["load-balancer", "task-queue"]
+```
+
+## Integration Points
+
+### With Other Optimization Agents
+- **Performance Monitor**: Provides real-time metrics for load balancing decisions
+- **Topology Optimizer**: Coordinates topology changes based on load patterns
+- **Resource Allocator**: Optimizes resource distribution across the swarm
+
+### With Swarm Infrastructure
+- **Task Orchestrator**: Receives load-balanced task assignments
+- **Agent Coordinator**: Provides agent capacity and availability information
+- **Memory System**: Stores load balancing history and patterns
+
+## Performance Metrics
+
+### Key Performance Indicators
+- **Load Distribution Variance**: Measure of load balance across agents
+- **Task Migration Rate**: Frequency of work-stealing operations
+- **Queue Latency**: Average time tasks spend in queues
+- **Utilization Efficiency**: Percentage of optimal resource utilization
+- **Fairness Index**: Measure of fair resource allocation
+
+### Benchmarking
+```javascript
+// Load balancer benchmarking suite
+const benchmarks = {
+  async throughputTest(taskCount, agentCount) {
+    const startTime = performance.now();
+    await this.distributeAndExecute(taskCount, agentCount);
+    const endTime = performance.now();
+    
+    return {
+      throughput: taskCount / ((endTime - startTime) / 1000),
+      averageLatency: (endTime - startTime) / taskCount
+    };
+  },
+  
+  async loadBalanceEfficiency(tasks, agents) {
+    const distribution = await this.distributeLoad(tasks, agents);
+    const idealLoad = tasks.length / agents.length;
+    
+    const variance = distribution.reduce((sum, load) => 
+      sum + Math.pow(load - idealLoad, 2), 0) / agents.length;
+    
+    return {
+      efficiency: 1 / (1 + variance),
+      loadVariance: variance
+    };
+  }
+};
+```
+
+This Load Balancing Coordinator agent provides comprehensive task distribution optimization with advanced algorithms, real-time monitoring, and adaptive resource allocation capabilities for high-performance swarm coordination.

.claude/agents/optimization/performance-monitor.md

@@ -0,0 +1,672 @@
+---
+name: Performance Monitor
+type: agent
+category: optimization
+description: Real-time metrics collection, bottleneck analysis, SLA monitoring and anomaly detection
+---
+
+# Performance Monitor Agent
+
+## Agent Profile
+- **Name**: Performance Monitor
+- **Type**: Performance Optimization Agent
+- **Specialization**: Real-time metrics collection and bottleneck analysis
+- **Performance Focus**: SLA monitoring, resource tracking, and anomaly detection
+
+## Core Capabilities
+
+### 1. Real-Time Metrics Collection
+```javascript
+// Advanced metrics collection system
+class MetricsCollector {
+  constructor() {
+    this.collectors = new Map();
+    this.aggregators = new Map();
+    this.streams = new Map();
+    this.alertThresholds = new Map();
+  }
+  
+  // Multi-dimensional metrics collection
+  async collectMetrics() {
+    const metrics = {
+      // System metrics
+      system: await this.collectSystemMetrics(),
+      
+      // Agent-specific metrics
+      agents: await this.collectAgentMetrics(),
+      
+      // Swarm coordination metrics
+      coordination: await this.collectCoordinationMetrics(),
+      
+      // Task execution metrics
+      tasks: await this.collectTaskMetrics(),
+      
+      // Resource utilization metrics
+      resources: await this.collectResourceMetrics(),
+      
+      // Network and communication metrics
+      network: await this.collectNetworkMetrics()
+    };
+    
+    // Real-time processing and analysis
+    await this.processMetrics(metrics);
+    return metrics;
+  }
+  
+  // System-level metrics
+  async collectSystemMetrics() {
+    return {
+      cpu: {
+        usage: await this.getCPUUsage(),
+        loadAverage: await this.getLoadAverage(),
+        coreUtilization: await this.getCoreUtilization()
+      },
+      memory: {
+        usage: await this.getMemoryUsage(),
+        available: await this.getAvailableMemory(),
+        pressure: await this.getMemoryPressure()
+      },
+      io: {
+        diskUsage: await this.getDiskUsage(),
+        diskIO: await this.getDiskIOStats(),
+        networkIO: await this.getNetworkIOStats()
+      },
+      processes: {
+        count: await this.getProcessCount(),
+        threads: await this.getThreadCount(),
+        handles: await this.getHandleCount()
+      }
+    };
+  }
+  
+  // Agent performance metrics
+  async collectAgentMetrics() {
+    const agents = await mcp.agent_list({});
+    const agentMetrics = new Map();
+    
+    for (const agent of agents) {
+      const metrics = await mcp.agent_metrics({ agentId: agent.id });
+      agentMetrics.set(agent.id, {
+        ...metrics,
+        efficiency: this.calculateEfficiency(metrics),
+        responsiveness: this.calculateResponsiveness(metrics),
+        reliability: this.calculateReliability(metrics)
+      });
+    }
+    
+    return agentMetrics;
+  }
+}
+```
+
+### 2. Bottleneck Detection & Analysis
+```javascript
+// Intelligent bottleneck detection
+class BottleneckAnalyzer {
+  constructor() {
+    this.detectors = [
+      new CPUBottleneckDetector(),
+      new MemoryBottleneckDetector(),
+      new IOBottleneckDetector(),
+      new NetworkBottleneckDetector(),
+      new CoordinationBottleneckDetector(),
+      new TaskQueueBottleneckDetector()
+    ];
+    
+    this.patterns = new Map();
+    this.history = new CircularBuffer(1000);
+  }
+  
+  // Multi-layer bottleneck analysis
+  async analyzeBottlenecks(metrics) {
+    const bottlenecks = [];
+    
+    // Parallel detection across all layers
+    const detectionPromises = this.detectors.map(detector => 
+      detector.detect(metrics)
+    );
+    
+    const results = await Promise.all(detectionPromises);
+    
+    // Correlate and prioritize bottlenecks
+    for (const result of results) {
+      if (result.detected) {
+        bottlenecks.push({
+          type: result.type,
+          severity: result.severity,
+          component: result.component,
+          rootCause: result.rootCause,
+          impact: result.impact,
+          recommendations: result.recommendations,
+          timestamp: Date.now()
+        });
+      }
+    }
+    
+    // Pattern recognition for recurring bottlenecks
+    await this.updatePatterns(bottlenecks);
+    
+    return this.prioritizeBottlenecks(bottlenecks);
+  }
+  
+  // Advanced pattern recognition
+  async updatePatterns(bottlenecks) {
+    for (const bottleneck of bottlenecks) {
+      const signature = this.createBottleneckSignature(bottleneck);
+      
+      if (this.patterns.has(signature)) {
+        const pattern = this.patterns.get(signature);
+        pattern.frequency++;
+        pattern.lastOccurrence = Date.now();
+        pattern.averageInterval = this.calculateAverageInterval(pattern);
+      } else {
+        this.patterns.set(signature, {
+          signature,
+          frequency: 1,
+          firstOccurrence: Date.now(),
+          lastOccurrence: Date.now(),
+          averageInterval: 0,
+          predictedNext: null
+        });
+      }
+    }
+  }
+}
+```
+
+### 3. SLA Monitoring & Alerting
+```javascript
+// Service Level Agreement monitoring
+class SLAMonitor {
+  constructor() {
+    this.slaDefinitions = new Map();
+    this.violations = new Map();
+    this.alertChannels = new Set();
+    this.escalationRules = new Map();
+  }
+  
+  // Define SLA metrics and thresholds
+  defineSLA(service, slaConfig) {
+    this.slaDefinitions.set(service, {
+      availability: slaConfig.availability || 99.9, // percentage
+      responseTime: slaConfig.responseTime || 1000, // milliseconds
+      throughput: slaConfig.throughput || 100, // requests per second
+      errorRate: slaConfig.errorRate || 0.1, // percentage
+      recoveryTime: slaConfig.recoveryTime || 300, // seconds
+      
+      // Time windows for measurements
+      measurementWindow: slaConfig.measurementWindow || 300, // seconds
+      evaluationInterval: slaConfig.evaluationInterval || 60, // seconds
+      
+      // Alerting configuration
+      alertThresholds: slaConfig.alertThresholds || {
+        warning: 0.8, // 80% of SLA threshold
+        critical: 0.9, // 90% of SLA threshold
+        breach: 1.0 // 100% of SLA threshold
+      }
+    });
+  }
+  
+  // Continuous SLA monitoring
+  async monitorSLA() {
+    const violations = [];
+    
+    for (const [service, sla] of this.slaDefinitions) {
+      const metrics = await this.getServiceMetrics(service);
+      const evaluation = this.evaluateSLA(service, sla, metrics);
+      
+      if (evaluation.violated) {
+        violations.push(evaluation);
+        await this.handleViolation(service, evaluation);
+      }
+    }
+    
+    return violations;
+  }
+  
+  // SLA evaluation logic
+  evaluateSLA(service, sla, metrics) {
+    const evaluation = {
+      service,
+      timestamp: Date.now(),
+      violated: false,
+      violations: []
+    };
+    
+    // Availability check
+    if (metrics.availability < sla.availability) {
+      evaluation.violations.push({
+        metric: 'availability',
+        expected: sla.availability,
+        actual: metrics.availability,
+        severity: this.calculateSeverity(metrics.availability, sla.availability, sla.alertThresholds)
+      });
+      evaluation.violated = true;
+    }
+    
+    // Response time check
+    if (metrics.responseTime > sla.responseTime) {
+      evaluation.violations.push({
+        metric: 'responseTime',
+        expected: sla.responseTime,
+        actual: metrics.responseTime,
+        severity: this.calculateSeverity(metrics.responseTime, sla.responseTime, sla.alertThresholds)
+      });
+      evaluation.violated = true;
+    }
+    
+    // Additional SLA checks...
+    
+    return evaluation;
+  }
+}
+```
+
+### 4. Resource Utilization Tracking
+```javascript
+// Comprehensive resource tracking
+class ResourceTracker {
+  constructor() {
+    this.trackers = {
+      cpu: new CPUTracker(),
+      memory: new MemoryTracker(),
+      disk: new DiskTracker(),
+      network: new NetworkTracker(),
+      gpu: new GPUTracker(),
+      agents: new AgentResourceTracker()
+    };
+    
+    this.forecaster = new ResourceForecaster();
+    this.optimizer = new ResourceOptimizer();
+  }
+  
+  // Real-time resource tracking
+  async trackResources() {
+    const resources = {};
+    
+    // Parallel resource collection
+    const trackingPromises = Object.entries(this.trackers).map(
+      async ([type, tracker]) => [type, await tracker.collect()]
+    );
+    
+    const results = await Promise.all(trackingPromises);
+    
+    for (const [type, data] of results) {
+      resources[type] = {
+        ...data,
+        utilization: this.calculateUtilization(data),
+        efficiency: this.calculateEfficiency(data),
+        trend: this.calculateTrend(type, data),
+        forecast: await this.forecaster.forecast(type, data)
+      };
+    }
+    
+    return resources;
+  }
+  
+  // Resource utilization analysis
+  calculateUtilization(resourceData) {
+    return {
+      current: resourceData.used / resourceData.total,
+      peak: resourceData.peak / resourceData.total,
+      average: resourceData.average / resourceData.total,
+      percentiles: {
+        p50: resourceData.p50 / resourceData.total,
+        p90: resourceData.p90 / resourceData.total,
+        p95: resourceData.p95 / resourceData.total,
+        p99: resourceData.p99 / resourceData.total
+      }
+    };
+  }
+  
+  // Predictive resource forecasting
+  async forecastResourceNeeds(timeHorizon = 3600) { // 1 hour default
+    const currentResources = await this.trackResources();
+    const forecasts = {};
+    
+    for (const [type, data] of Object.entries(currentResources)) {
+      forecasts[type] = await this.forecaster.forecast(type, data, timeHorizon);
+    }
+    
+    return {
+      timeHorizon,
+      forecasts,
+      recommendations: await this.optimizer.generateRecommendations(forecasts),
+      confidence: this.calculateForecastConfidence(forecasts)
+    };
+  }
+}
+```
+
+## MCP Integration Hooks
+
+### Performance Data Collection
+```javascript
+// Comprehensive MCP integration
+const performanceIntegration = {
+  // Real-time performance monitoring
+  async startMonitoring(config = {}) {
+    const monitoringTasks = [
+      this.monitorSwarmHealth(),
+      this.monitorAgentPerformance(),
+      this.monitorResourceUtilization(),
+      this.monitorBottlenecks(),
+      this.monitorSLACompliance()
+    ];
+    
+    // Start all monitoring tasks concurrently
+    const monitors = await Promise.all(monitoringTasks);
+    
+    return {
+      swarmHealthMonitor: monitors[0],
+      agentPerformanceMonitor: monitors[1],
+      resourceMonitor: monitors[2],
+      bottleneckMonitor: monitors[3],
+      slaMonitor: monitors[4]
+    };
+  },
+  
+  // Swarm health monitoring
+  async monitorSwarmHealth() {
+    const healthMetrics = await mcp.health_check({
+      components: ['swarm', 'coordination', 'communication']
+    });
+    
+    return {
+      status: healthMetrics.overall,
+      components: healthMetrics.components,
+      issues: healthMetrics.issues,
+      recommendations: healthMetrics.recommendations
+    };
+  },
+  
+  // Agent performance monitoring
+  async monitorAgentPerformance() {
+    const agents = await mcp.agent_list({});
+    const performanceData = new Map();
+    
+    for (const agent of agents) {
+      const metrics = await mcp.agent_metrics({ agentId: agent.id });
+      const performance = await mcp.performance_report({
+        format: 'detailed',
+        timeframe: '24h'
+      });
+      
+      performanceData.set(agent.id, {
+        ...metrics,
+        performance,
+        efficiency: this.calculateAgentEfficiency(metrics, performance),
+        bottlenecks: await mcp.bottleneck_analyze({ component: agent.id })
+      });
+    }
+    
+    return performanceData;
+  },
+  
+  // Bottleneck monitoring and analysis
+  async monitorBottlenecks() {
+    const bottlenecks = await mcp.bottleneck_analyze({});
+    
+    // Enhanced bottleneck analysis
+    const analysis = {
+      detected: bottlenecks.length > 0,
+      count: bottlenecks.length,
+      severity: this.calculateOverallSeverity(bottlenecks),
+      categories: this.categorizeBottlenecks(bottlenecks),
+      trends: await this.analyzeBottleneckTrends(bottlenecks),
+      predictions: await this.predictBottlenecks(bottlenecks)
+    };
+    
+    return analysis;
+  }
+};
+```
+
+### Anomaly Detection
+```javascript
+// Advanced anomaly detection system
+class AnomalyDetector {
+  constructor() {
+    this.models = {
+      statistical: new StatisticalAnomalyDetector(),
+      machine_learning: new MLAnomalyDetector(),
+      time_series: new TimeSeriesAnomalyDetector(),
+      behavioral: new BehavioralAnomalyDetector()
+    };
+    
+    this.ensemble = new EnsembleDetector(this.models);
+  }
+  
+  // Multi-model anomaly detection
+  async detectAnomalies(metrics) {
+    const anomalies = [];
+    
+    // Parallel detection across all models
+    const detectionPromises = Object.entries(this.models).map(
+      async ([modelType, model]) => {
+        const detected = await model.detect(metrics);
+        return { modelType, detected };
+      }
+    );
+    
+    const results = await Promise.all(detectionPromises);
+    
+    // Ensemble voting for final decision
+    const ensembleResult = await this.ensemble.vote(results);
+    
+    return {
+      anomalies: ensembleResult.anomalies,
+      confidence: ensembleResult.confidence,
+      consensus: ensembleResult.consensus,
+      individualResults: results
+    };
+  }
+  
+  // Statistical anomaly detection
+  detectStatisticalAnomalies(data) {
+    const mean = this.calculateMean(data);
+    const stdDev = this.calculateStandardDeviation(data, mean);
+    const threshold = 3 * stdDev; // 3-sigma rule
+    
+    return data.filter(point => Math.abs(point - mean) > threshold)
+               .map(point => ({
+                 value: point,
+                 type: 'statistical',
+                 deviation: Math.abs(point - mean) / stdDev,
+                 probability: this.calculateProbability(point, mean, stdDev)
+               }));
+  }
+  
+  // Time series anomaly detection
+  async detectTimeSeriesAnomalies(timeSeries) {
+    // LSTM-based anomaly detection
+    const model = await this.loadTimeSeriesModel();
+    const predictions = await model.predict(timeSeries);
+    
+    const anomalies = [];
+    for (let i = 0; i < timeSeries.length; i++) {
+      const error = Math.abs(timeSeries[i] - predictions[i]);
+      const threshold = this.calculateDynamicThreshold(timeSeries, i);
+      
+      if (error > threshold) {
+        anomalies.push({
+          timestamp: i,
+          actual: timeSeries[i],
+          predicted: predictions[i],
+          error: error,
+          type: 'time_series'
+        });
+      }
+    }
+    
+    return anomalies;
+  }
+}
+```
+
+## Dashboard Integration
+
+### Real-Time Performance Dashboard
+```javascript
+// Dashboard data provider
+class DashboardProvider {
+  constructor() {
+    this.updateInterval = 1000; // 1 second updates
+    this.subscribers = new Set();
+    this.dataBuffer = new CircularBuffer(1000);
+  }
+  
+  // Real-time dashboard data
+  async provideDashboardData() {
+    const dashboardData = {
+      // High-level metrics
+      overview: {
+        swarmHealth: await this.getSwarmHealthScore(),
+        activeAgents: await this.getActiveAgentCount(),
+        totalTasks: await this.getTotalTaskCount(),
+        averageResponseTime: await this.getAverageResponseTime()
+      },
+      
+      // Performance metrics
+      performance: {
+        throughput: await this.getCurrentThroughput(),
+        latency: await this.getCurrentLatency(),
+        errorRate: await this.getCurrentErrorRate(),
+        utilization: await this.getResourceUtilization()
+      },
+      
+      // Real-time charts data
+      timeSeries: {
+        cpu: this.getCPUTimeSeries(),
+        memory: this.getMemoryTimeSeries(),
+        network: this.getNetworkTimeSeries(),
+        tasks: this.getTaskTimeSeries()
+      },
+      
+      // Alerts and notifications
+      alerts: await this.getActiveAlerts(),
+      notifications: await this.getRecentNotifications(),
+      
+      // Agent status
+      agents: await this.getAgentStatusSummary(),
+      
+      timestamp: Date.now()
+    };
+    
+    // Broadcast to subscribers
+    this.broadcast(dashboardData);
+    
+    return dashboardData;
+  }
+  
+  // WebSocket subscription management
+  subscribe(callback) {
+    this.subscribers.add(callback);
+    return () => this.subscribers.delete(callback);
+  }
+  
+  broadcast(data) {
+    this.subscribers.forEach(callback => {
+      try {
+        callback(data);
+      } catch (error) {
+        console.error('Dashboard subscriber error:', error);
+      }
+    });
+  }
+}
+```
+
+## Operational Commands
+
+### Monitoring Commands
+```bash
+# Start comprehensive monitoring
+npx claude-flow performance-report --format detailed --timeframe 24h
+
+# Real-time bottleneck analysis
+npx claude-flow bottleneck-analyze --component swarm-coordination
+
+# Health check all components
+npx claude-flow health-check --components ["swarm", "agents", "coordination"]
+
+# Collect specific metrics
+npx claude-flow metrics-collect --components ["cpu", "memory", "network"]
+
+# Monitor SLA compliance
+npx claude-flow sla-monitor --service swarm-coordination --threshold 99.9
+```
+
+### Alert Configuration
+```bash
+# Configure performance alerts
+npx claude-flow alert-config --metric cpu_usage --threshold 80 --severity warning
+
+# Set up anomaly detection
+npx claude-flow anomaly-setup --models ["statistical", "ml", "time_series"]
+
+# Configure notification channels
+npx claude-flow notification-config --channels ["slack", "email", "webhook"]
+```
+
+## Integration Points
+
+### With Other Optimization Agents
+- **Load Balancer**: Provides performance data for load balancing decisions
+- **Topology Optimizer**: Supplies network and coordination metrics
+- **Resource Manager**: Shares resource utilization and forecasting data
+
+### With Swarm Infrastructure
+- **Task Orchestrator**: Monitors task execution performance
+- **Agent Coordinator**: Tracks agent health and performance
+- **Memory System**: Stores historical performance data and patterns
+
+## Performance Analytics
+
+### Key Metrics Dashboard
+```javascript
+// Performance analytics engine
+const analytics = {
+  // Key Performance Indicators
+  calculateKPIs(metrics) {
+    return {
+      // Availability metrics
+      uptime: this.calculateUptime(metrics),
+      availability: this.calculateAvailability(metrics),
+      
+      // Performance metrics
+      responseTime: {
+        average: this.calculateAverage(metrics.responseTimes),
+        p50: this.calculatePercentile(metrics.responseTimes, 50),
+        p90: this.calculatePercentile(metrics.responseTimes, 90),
+        p95: this.calculatePercentile(metrics.responseTimes, 95),
+        p99: this.calculatePercentile(metrics.responseTimes, 99)
+      },
+      
+      // Throughput metrics
+      throughput: this.calculateThroughput(metrics),
+      
+      // Error metrics
+      errorRate: this.calculateErrorRate(metrics),
+      
+      // Resource efficiency
+      resourceEfficiency: this.calculateResourceEfficiency(metrics),
+      
+      // Cost metrics
+      costEfficiency: this.calculateCostEfficiency(metrics)
+    };
+  },
+  
+  // Trend analysis
+  analyzeTrends(historicalData, timeWindow = '7d') {
+    return {
+      performance: this.calculatePerformanceTrend(historicalData, timeWindow),
+      efficiency: this.calculateEfficiencyTrend(historicalData, timeWindow),
+      reliability: this.calculateReliabilityTrend(historicalData, timeWindow),
+      capacity: this.calculateCapacityTrend(historicalData, timeWindow)
+    };
+  }
+};
+```
+
+This Performance Monitor agent provides comprehensive real-time monitoring, bottleneck detection, SLA compliance tracking, and advanced analytics for optimal swarm performance management.

.claude/agents/optimization/resource-allocator.md

@@ -0,0 +1,674 @@
+---
+name: Resource Allocator
+type: agent
+category: optimization
+description: Adaptive resource allocation, predictive scaling and intelligent capacity planning
+---
+
+# Resource Allocator Agent
+
+## Agent Profile
+- **Name**: Resource Allocator
+- **Type**: Performance Optimization Agent
+- **Specialization**: Adaptive resource allocation and predictive scaling
+- **Performance Focus**: Intelligent resource management and capacity planning
+
+## Core Capabilities
+
+### 1. Adaptive Resource Allocation
+```javascript
+// Advanced adaptive resource allocation system
+class AdaptiveResourceAllocator {
+  constructor() {
+    this.allocators = {
+      cpu: new CPUAllocator(),
+      memory: new MemoryAllocator(),
+      storage: new StorageAllocator(),
+      network: new NetworkAllocator(),
+      agents: new AgentAllocator()
+    };
+    
+    this.predictor = new ResourcePredictor();
+    this.optimizer = new AllocationOptimizer();
+    this.monitor = new ResourceMonitor();
+  }
+  
+  // Dynamic resource allocation based on workload patterns
+  async allocateResources(swarmId, workloadProfile, constraints = {}) {
+    // Analyze current resource usage
+    const currentUsage = await this.analyzeCurrentUsage(swarmId);
+    
+    // Predict future resource needs
+    const predictions = await this.predictor.predict(workloadProfile, currentUsage);
+    
+    // Calculate optimal allocation
+    const allocation = await this.optimizer.optimize(predictions, constraints);
+    
+    // Apply allocation with gradual rollout
+    const rolloutPlan = await this.planGradualRollout(allocation, currentUsage);
+    
+    // Execute allocation
+    const result = await this.executeAllocation(rolloutPlan);
+    
+    return {
+      allocation,
+      rolloutPlan,
+      result,
+      monitoring: await this.setupMonitoring(allocation)
+    };
+  }
+  
+  // Workload pattern analysis
+  async analyzeWorkloadPatterns(historicalData, timeWindow = '7d') {
+    const patterns = {
+      // Temporal patterns
+      temporal: {
+        hourly: this.analyzeHourlyPatterns(historicalData),
+        daily: this.analyzeDailyPatterns(historicalData),
+        weekly: this.analyzeWeeklyPatterns(historicalData),
+        seasonal: this.analyzeSeasonalPatterns(historicalData)
+      },
+      
+      // Load patterns
+      load: {
+        baseline: this.calculateBaselineLoad(historicalData),
+        peaks: this.identifyPeakPatterns(historicalData),
+        valleys: this.identifyValleyPatterns(historicalData),
+        spikes: this.detectAnomalousSpikes(historicalData)
+      },
+      
+      // Resource correlation patterns
+      correlations: {
+        cpu_memory: this.analyzeCPUMemoryCorrelation(historicalData),
+        network_load: this.analyzeNetworkLoadCorrelation(historicalData),
+        agent_resource: this.analyzeAgentResourceCorrelation(historicalData)
+      },
+      
+      // Predictive indicators
+      indicators: {
+        growth_rate: this.calculateGrowthRate(historicalData),
+        volatility: this.calculateVolatility(historicalData),
+        predictability: this.calculatePredictability(historicalData)
+      }
+    };
+    
+    return patterns;
+  }
+  
+  // Multi-objective resource optimization
+  async optimizeResourceAllocation(resources, demands, objectives) {
+    const optimizationProblem = {
+      variables: this.defineOptimizationVariables(resources),
+      constraints: this.defineConstraints(resources, demands),
+      objectives: this.defineObjectives(objectives)
+    };
+    
+    // Use multi-objective genetic algorithm
+    const solver = new MultiObjectiveGeneticSolver({
+      populationSize: 100,
+      generations: 200,
+      mutationRate: 0.1,
+      crossoverRate: 0.8
+    });
+    
+    const solutions = await solver.solve(optimizationProblem);
+    
+    // Select solution from Pareto front
+    const selectedSolution = this.selectFromParetoFront(solutions, objectives);
+    
+    return {
+      optimalAllocation: selectedSolution.allocation,
+      paretoFront: solutions.paretoFront,
+      tradeoffs: solutions.tradeoffs,
+      confidence: selectedSolution.confidence
+    };
+  }
+}
+```
+
+### 2. Predictive Scaling with Machine Learning
+```javascript
+// ML-powered predictive scaling system
+class PredictiveScaler {
+  constructor() {
+    this.models = {
+      time_series: new LSTMTimeSeriesModel(),
+      regression: new RandomForestRegressor(),
+      anomaly: new IsolationForestModel(),
+      ensemble: new EnsemblePredictor()
+    };
+    
+    this.featureEngineering = new FeatureEngineer();
+    this.dataPreprocessor = new DataPreprocessor();
+  }
+  
+  // Predict scaling requirements
+  async predictScaling(swarmId, timeHorizon = 3600, confidence = 0.95) {
+    // Collect training data
+    const trainingData = await this.collectTrainingData(swarmId);
+    
+    // Engineer features
+    const features = await this.featureEngineering.engineer(trainingData);
+    
+    // Train/update models
+    await this.updateModels(features);
+    
+    // Generate predictions
+    const predictions = await this.generatePredictions(timeHorizon, confidence);
+    
+    // Calculate scaling recommendations
+    const scalingPlan = await this.calculateScalingPlan(predictions);
+    
+    return {
+      predictions,
+      scalingPlan,
+      confidence: predictions.confidence,
+      timeHorizon,
+      features: features.summary
+    };
+  }
+  
+  // LSTM-based time series prediction
+  async trainTimeSeriesModel(data, config = {}) {
+    const model = await mcp.neural_train({
+      pattern_type: 'prediction',
+      training_data: JSON.stringify({
+        sequences: data.sequences,
+        targets: data.targets,
+        features: data.features
+      }),
+      epochs: config.epochs || 100
+    });
+    
+    // Validate model performance
+    const validation = await this.validateModel(model, data.validation);
+    
+    if (validation.accuracy > 0.85) {
+      await mcp.model_save({
+        modelId: model.modelId,
+        path: '/models/scaling_predictor.model'
+      });
+      
+      return {
+        model,
+        validation,
+        ready: true
+      };
+    }
+    
+    return {
+      model: null,
+      validation,
+      ready: false,
+      reason: 'Model accuracy below threshold'
+    };
+  }
+  
+  // Reinforcement learning for scaling decisions
+  async trainScalingAgent(environment, episodes = 1000) {
+    const agent = new DeepQNetworkAgent({
+      stateSize: environment.stateSize,
+      actionSize: environment.actionSize,
+      learningRate: 0.001,
+      epsilon: 1.0,
+      epsilonDecay: 0.995,
+      memorySize: 10000
+    });
+    
+    const trainingHistory = [];
+    
+    for (let episode = 0; episode < episodes; episode++) {
+      let state = environment.reset();
+      let totalReward = 0;
+      let done = false;
+      
+      while (!done) {
+        // Agent selects action
+        const action = agent.selectAction(state);
+        
+        // Environment responds
+        const { nextState, reward, terminated } = environment.step(action);
+        
+        // Agent learns from experience
+        agent.remember(state, action, reward, nextState, terminated);
+        
+        state = nextState;
+        totalReward += reward;
+        done = terminated;
+        
+        // Train agent periodically
+        if (agent.memory.length > agent.batchSize) {
+          await agent.train();
+        }
+      }
+      
+      trainingHistory.push({
+        episode,
+        reward: totalReward,
+        epsilon: agent.epsilon
+      });
+      
+      // Log progress
+      if (episode % 100 === 0) {
+        console.log(`Episode ${episode}: Reward ${totalReward}, Epsilon ${agent.epsilon}`);
+      }
+    }
+    
+    return {
+      agent,
+      trainingHistory,
+      performance: this.evaluateAgentPerformance(trainingHistory)
+    };
+  }
+}
+```
+
+### 3. Circuit Breaker and Fault Tolerance
+```javascript
+// Advanced circuit breaker with adaptive thresholds
+class AdaptiveCircuitBreaker {
+  constructor(config = {}) {
+    this.failureThreshold = config.failureThreshold || 5;
+    this.recoveryTimeout = config.recoveryTimeout || 60000;
+    this.successThreshold = config.successThreshold || 3;
+    
+    this.state = 'CLOSED'; // CLOSED, OPEN, HALF_OPEN
+    this.failureCount = 0;
+    this.successCount = 0;
+    this.lastFailureTime = null;
+    
+    // Adaptive thresholds
+    this.adaptiveThresholds = new AdaptiveThresholdManager();
+    this.performanceHistory = new CircularBuffer(1000);
+    
+    // Metrics
+    this.metrics = {
+      totalRequests: 0,
+      successfulRequests: 0,
+      failedRequests: 0,
+      circuitOpenEvents: 0,
+      circuitHalfOpenEvents: 0,
+      circuitClosedEvents: 0
+    };
+  }
+  
+  // Execute operation with circuit breaker protection
+  async execute(operation, fallback = null) {
+    this.metrics.totalRequests++;
+    
+    // Check circuit state
+    if (this.state === 'OPEN') {
+      if (this.shouldAttemptReset()) {
+        this.state = 'HALF_OPEN';
+        this.successCount = 0;
+        this.metrics.circuitHalfOpenEvents++;
+      } else {
+        return await this.executeFallback(fallback);
+      }
+    }
+    
+    try {
+      const startTime = performance.now();
+      const result = await operation();
+      const endTime = performance.now();
+      
+      // Record success
+      this.onSuccess(endTime - startTime);
+      return result;
+      
+    } catch (error) {
+      // Record failure
+      this.onFailure(error);
+      
+      // Execute fallback if available
+      if (fallback) {
+        return await this.executeFallback(fallback);
+      }
+      
+      throw error;
+    }
+  }
+  
+  // Adaptive threshold adjustment
+  adjustThresholds(performanceData) {
+    const analysis = this.adaptiveThresholds.analyze(performanceData);
+    
+    if (analysis.recommendAdjustment) {
+      this.failureThreshold = Math.max(
+        1, 
+        Math.round(this.failureThreshold * analysis.thresholdMultiplier)
+      );
+      
+      this.recoveryTimeout = Math.max(
+        1000,
+        Math.round(this.recoveryTimeout * analysis.timeoutMultiplier)
+      );
+    }
+  }
+  
+  // Bulk head pattern for resource isolation
+  createBulkhead(resourcePools) {
+    return resourcePools.map(pool => ({
+      name: pool.name,
+      capacity: pool.capacity,
+      queue: new PriorityQueue(),
+      semaphore: new Semaphore(pool.capacity),
+      circuitBreaker: new AdaptiveCircuitBreaker(pool.config),
+      metrics: new BulkheadMetrics()
+    }));
+  }
+}
+```
+
+### 4. Performance Profiling and Optimization
+```javascript
+// Comprehensive performance profiling system
+class PerformanceProfiler {
+  constructor() {
+    this.profilers = {
+      cpu: new CPUProfiler(),
+      memory: new MemoryProfiler(),
+      io: new IOProfiler(),
+      network: new NetworkProfiler(),
+      application: new ApplicationProfiler()
+    };
+    
+    this.analyzer = new ProfileAnalyzer();
+    this.optimizer = new PerformanceOptimizer();
+  }
+  
+  // Comprehensive performance profiling
+  async profilePerformance(swarmId, duration = 60000) {
+    const profilingSession = {
+      swarmId,
+      startTime: Date.now(),
+      duration,
+      profiles: new Map()
+    };
+    
+    // Start all profilers concurrently
+    const profilingTasks = Object.entries(this.profilers).map(
+      async ([type, profiler]) => {
+        const profile = await profiler.profile(duration);
+        return [type, profile];
+      }
+    );
+    
+    const profiles = await Promise.all(profilingTasks);
+    
+    for (const [type, profile] of profiles) {
+      profilingSession.profiles.set(type, profile);
+    }
+    
+    // Analyze performance data
+    const analysis = await this.analyzer.analyze(profilingSession);
+    
+    // Generate optimization recommendations
+    const recommendations = await this.optimizer.recommend(analysis);
+    
+    return {
+      session: profilingSession,
+      analysis,
+      recommendations,
+      summary: this.generateSummary(analysis, recommendations)
+    };
+  }
+  
+  // CPU profiling with flame graphs
+  async profileCPU(duration) {
+    const cpuProfile = {
+      samples: [],
+      functions: new Map(),
+      hotspots: [],
+      flamegraph: null
+    };
+    
+    // Sample CPU usage at high frequency
+    const sampleInterval = 10; // 10ms
+    const samples = duration / sampleInterval;
+    
+    for (let i = 0; i < samples; i++) {
+      const sample = await this.sampleCPU();
+      cpuProfile.samples.push(sample);
+      
+      // Update function statistics
+      this.updateFunctionStats(cpuProfile.functions, sample);
+      
+      await this.sleep(sampleInterval);
+    }
+    
+    // Generate flame graph
+    cpuProfile.flamegraph = this.generateFlameGraph(cpuProfile.samples);
+    
+    // Identify hotspots
+    cpuProfile.hotspots = this.identifyHotspots(cpuProfile.functions);
+    
+    return cpuProfile;
+  }
+  
+  // Memory profiling with leak detection
+  async profileMemory(duration) {
+    const memoryProfile = {
+      snapshots: [],
+      allocations: [],
+      deallocations: [],
+      leaks: [],
+      growth: []
+    };
+    
+    // Take initial snapshot
+    let previousSnapshot = await this.takeMemorySnapshot();
+    memoryProfile.snapshots.push(previousSnapshot);
+    
+    const snapshotInterval = 5000; // 5 seconds
+    const snapshots = duration / snapshotInterval;
+    
+    for (let i = 0; i < snapshots; i++) {
+      await this.sleep(snapshotInterval);
+      
+      const snapshot = await this.takeMemorySnapshot();
+      memoryProfile.snapshots.push(snapshot);
+      
+      // Analyze memory changes
+      const changes = this.analyzeMemoryChanges(previousSnapshot, snapshot);
+      memoryProfile.allocations.push(...changes.allocations);
+      memoryProfile.deallocations.push(...changes.deallocations);
+      
+      // Detect potential leaks
+      const leaks = this.detectMemoryLeaks(changes);
+      memoryProfile.leaks.push(...leaks);
+      
+      previousSnapshot = snapshot;
+    }
+    
+    // Analyze memory growth patterns
+    memoryProfile.growth = this.analyzeMemoryGrowth(memoryProfile.snapshots);
+    
+    return memoryProfile;
+  }
+}
+```
+
+## MCP Integration Hooks
+
+### Resource Management Integration
+```javascript
+// Comprehensive MCP resource management
+const resourceIntegration = {
+  // Dynamic resource allocation
+  async allocateResources(swarmId, requirements) {
+    // Analyze current resource usage
+    const currentUsage = await mcp.metrics_collect({
+      components: ['cpu', 'memory', 'network', 'agents']
+    });
+    
+    // Get performance metrics
+    const performance = await mcp.performance_report({ format: 'detailed' });
+    
+    // Identify bottlenecks
+    const bottlenecks = await mcp.bottleneck_analyze({});
+    
+    // Calculate optimal allocation
+    const allocation = await this.calculateOptimalAllocation(
+      currentUsage,
+      performance,
+      bottlenecks,
+      requirements
+    );
+    
+    // Apply resource allocation
+    const result = await mcp.daa_resource_alloc({
+      resources: allocation.resources,
+      agents: allocation.agents
+    });
+    
+    return {
+      allocation,
+      result,
+      monitoring: await this.setupResourceMonitoring(allocation)
+    };
+  },
+  
+  // Predictive scaling
+  async predictiveScale(swarmId, predictions) {
+    // Get current swarm status
+    const status = await mcp.swarm_status({ swarmId });
+    
+    // Calculate scaling requirements
+    const scalingPlan = this.calculateScalingPlan(status, predictions);
+    
+    if (scalingPlan.scaleRequired) {
+      // Execute scaling
+      const scalingResult = await mcp.swarm_scale({
+        swarmId,
+        targetSize: scalingPlan.targetSize
+      });
+      
+      // Optimize topology after scaling
+      if (scalingResult.success) {
+        await mcp.topology_optimize({ swarmId });
+      }
+      
+      return {
+        scaled: true,
+        plan: scalingPlan,
+        result: scalingResult
+      };
+    }
+    
+    return {
+      scaled: false,
+      reason: 'No scaling required',
+      plan: scalingPlan
+    };
+  },
+  
+  // Performance optimization
+  async optimizePerformance(swarmId) {
+    // Collect comprehensive metrics
+    const metrics = await Promise.all([
+      mcp.performance_report({ format: 'json' }),
+      mcp.bottleneck_analyze({}),
+      mcp.agent_metrics({}),
+      mcp.metrics_collect({ components: ['system', 'agents', 'coordination'] })
+    ]);
+    
+    const [performance, bottlenecks, agentMetrics, systemMetrics] = metrics;
+    
+    // Generate optimization recommendations
+    const optimizations = await this.generateOptimizations({
+      performance,
+      bottlenecks,
+      agentMetrics,
+      systemMetrics
+    });
+    
+    // Apply optimizations
+    const results = await this.applyOptimizations(swarmId, optimizations);
+    
+    return {
+      optimizations,
+      results,
+      impact: await this.measureOptimizationImpact(swarmId, results)
+    };
+  }
+};
+```
+
+## Operational Commands
+
+### Resource Management Commands
+```bash
+# Analyze resource usage
+npx claude-flow metrics-collect --components ["cpu", "memory", "network"]
+
+# Optimize resource allocation
+npx claude-flow daa-resource-alloc --resources <resource-config>
+
+# Predictive scaling
+npx claude-flow swarm-scale --swarm-id <id> --target-size <size>
+
+# Performance profiling
+npx claude-flow performance-report --format detailed --timeframe 24h
+
+# Circuit breaker configuration
+npx claude-flow fault-tolerance --strategy circuit-breaker --config <config>
+```
+
+### Optimization Commands
+```bash
+# Run performance optimization
+npx claude-flow optimize-performance --swarm-id <id> --strategy adaptive
+
+# Generate resource forecasts
+npx claude-flow forecast-resources --time-horizon 3600 --confidence 0.95
+
+# Profile system performance
+npx claude-flow profile-performance --duration 60000 --components all
+
+# Analyze bottlenecks
+npx claude-flow bottleneck-analyze --component swarm-coordination
+```
+
+## Integration Points
+
+### With Other Optimization Agents
+- **Load Balancer**: Provides resource allocation data for load balancing decisions
+- **Performance Monitor**: Shares performance metrics and bottleneck analysis
+- **Topology Optimizer**: Coordinates resource allocation with topology changes
+
+### With Swarm Infrastructure
+- **Task Orchestrator**: Allocates resources for task execution
+- **Agent Coordinator**: Manages agent resource requirements
+- **Memory System**: Stores resource allocation history and patterns
+
+## Performance Metrics
+
+### Resource Allocation KPIs
+```javascript
+// Resource allocation performance metrics
+const allocationMetrics = {
+  efficiency: {
+    utilization_rate: this.calculateUtilizationRate(),
+    waste_percentage: this.calculateWastePercentage(),
+    allocation_accuracy: this.calculateAllocationAccuracy(),
+    prediction_accuracy: this.calculatePredictionAccuracy()
+  },
+  
+  performance: {
+    allocation_latency: this.calculateAllocationLatency(),
+    scaling_response_time: this.calculateScalingResponseTime(),
+    optimization_impact: this.calculateOptimizationImpact(),
+    cost_efficiency: this.calculateCostEfficiency()
+  },
+  
+  reliability: {
+    availability: this.calculateAvailability(),
+    fault_tolerance: this.calculateFaultTolerance(),
+    recovery_time: this.calculateRecoveryTime(),
+    circuit_breaker_effectiveness: this.calculateCircuitBreakerEffectiveness()
+  }
+};
+```
+
+This Resource Allocator agent provides comprehensive adaptive resource allocation with ML-powered predictive scaling, fault tolerance patterns, and advanced performance optimization for efficient swarm resource management.

.claude/agents/optimization/topology-optimizer.md

@@ -0,0 +1,808 @@
+---
+name: Topology Optimizer
+type: agent
+category: optimization
+description: Dynamic swarm topology reconfiguration and communication pattern optimization
+---
+
+# Topology Optimizer Agent
+
+## Agent Profile
+- **Name**: Topology Optimizer
+- **Type**: Performance Optimization Agent
+- **Specialization**: Dynamic swarm topology reconfiguration and network optimization
+- **Performance Focus**: Communication pattern optimization and adaptive network structures
+
+## Core Capabilities
+
+### 1. Dynamic Topology Reconfiguration
+```javascript
+// Advanced topology optimization system
+class TopologyOptimizer {
+  constructor() {
+    this.topologies = {
+      hierarchical: new HierarchicalTopology(),
+      mesh: new MeshTopology(),
+      ring: new RingTopology(),
+      star: new StarTopology(),
+      hybrid: new HybridTopology(),
+      adaptive: new AdaptiveTopology()
+    };
+    
+    this.optimizer = new NetworkOptimizer();
+    this.analyzer = new TopologyAnalyzer();
+    this.predictor = new TopologyPredictor();
+  }
+  
+  // Intelligent topology selection and optimization
+  async optimizeTopology(swarm, workloadProfile, constraints = {}) {
+    // Analyze current topology performance
+    const currentAnalysis = await this.analyzer.analyze(swarm.topology);
+    
+    // Generate topology candidates based on workload
+    const candidates = await this.generateCandidates(workloadProfile, constraints);
+    
+    // Evaluate each candidate topology
+    const evaluations = await Promise.all(
+      candidates.map(candidate => this.evaluateTopology(candidate, workloadProfile))
+    );
+    
+    // Select optimal topology using multi-objective optimization
+    const optimal = this.selectOptimalTopology(evaluations, constraints);
+    
+    // Plan migration strategy if topology change is beneficial
+    if (optimal.improvement > constraints.minImprovement || 0.1) {
+      const migrationPlan = await this.planMigration(swarm.topology, optimal.topology);
+      return {
+        recommended: optimal.topology,
+        improvement: optimal.improvement,
+        migrationPlan,
+        estimatedDowntime: migrationPlan.estimatedDowntime,
+        benefits: optimal.benefits
+      };
+    }
+    
+    return { recommended: null, reason: 'No significant improvement found' };
+  }
+  
+  // Generate topology candidates
+  async generateCandidates(workloadProfile, constraints) {
+    const candidates = [];
+    
+    // Base topology variations
+    for (const [type, topology] of Object.entries(this.topologies)) {
+      if (this.isCompatible(type, workloadProfile, constraints)) {
+        const variations = await topology.generateVariations(workloadProfile);
+        candidates.push(...variations);
+      }
+    }
+    
+    // Hybrid topology generation
+    const hybrids = await this.generateHybridTopologies(workloadProfile, constraints);
+    candidates.push(...hybrids);
+    
+    // AI-generated novel topologies
+    const aiGenerated = await this.generateAITopologies(workloadProfile);
+    candidates.push(...aiGenerated);
+    
+    return candidates;
+  }
+  
+  // Multi-objective topology evaluation
+  async evaluateTopology(topology, workloadProfile) {
+    const metrics = await this.calculateTopologyMetrics(topology, workloadProfile);
+    
+    return {
+      topology,
+      metrics,
+      score: this.calculateOverallScore(metrics),
+      strengths: this.identifyStrengths(metrics),
+      weaknesses: this.identifyWeaknesses(metrics),
+      suitability: this.calculateSuitability(metrics, workloadProfile)
+    };
+  }
+}
+```
+
+### 2. Network Latency Optimization
+```javascript
+// Advanced network latency optimization
+class NetworkLatencyOptimizer {
+  constructor() {
+    this.latencyAnalyzer = new LatencyAnalyzer();
+    this.routingOptimizer = new RoutingOptimizer();
+    this.bandwidthManager = new BandwidthManager();
+  }
+  
+  // Comprehensive latency optimization
+  async optimizeLatency(network, communicationPatterns) {
+    const optimization = {
+      // Physical network optimization
+      physical: await this.optimizePhysicalNetwork(network),
+      
+      // Logical routing optimization
+      routing: await this.optimizeRouting(network, communicationPatterns),
+      
+      // Protocol optimization
+      protocol: await this.optimizeProtocols(network),
+      
+      // Caching strategies
+      caching: await this.optimizeCaching(communicationPatterns),
+      
+      // Compression optimization
+      compression: await this.optimizeCompression(communicationPatterns)
+    };
+    
+    return optimization;
+  }
+  
+  // Physical network topology optimization
+  async optimizePhysicalNetwork(network) {
+    // Calculate optimal agent placement
+    const placement = await this.calculateOptimalPlacement(network.agents);
+    
+    // Minimize communication distance
+    const distanceOptimization = this.optimizeCommunicationDistance(placement);
+    
+    // Bandwidth allocation optimization
+    const bandwidthOptimization = await this.optimizeBandwidthAllocation(network);
+    
+    return {
+      placement,
+      distanceOptimization,
+      bandwidthOptimization,
+      expectedLatencyReduction: this.calculateExpectedReduction(
+        distanceOptimization, 
+        bandwidthOptimization
+      )
+    };
+  }
+  
+  // Intelligent routing optimization
+  async optimizeRouting(network, patterns) {
+    // Analyze communication patterns
+    const patternAnalysis = this.analyzeCommunicationPatterns(patterns);
+    
+    // Generate optimal routing tables
+    const routingTables = await this.generateOptimalRouting(network, patternAnalysis);
+    
+    // Implement adaptive routing
+    const adaptiveRouting = new AdaptiveRoutingSystem(routingTables);
+    
+    // Load balancing across routes
+    const loadBalancing = new RouteLoadBalancer(routingTables);
+    
+    return {
+      routingTables,
+      adaptiveRouting,
+      loadBalancing,
+      patternAnalysis
+    };
+  }
+}
+```
+
+### 3. Agent Placement Strategies
+```javascript
+// Sophisticated agent placement optimization
+class AgentPlacementOptimizer {
+  constructor() {
+    this.algorithms = {
+      genetic: new GeneticPlacementAlgorithm(),
+      simulated_annealing: new SimulatedAnnealingPlacement(),
+      particle_swarm: new ParticleSwarmPlacement(),
+      graph_partitioning: new GraphPartitioningPlacement(),
+      machine_learning: new MLBasedPlacement()
+    };
+  }
+  
+  // Multi-algorithm agent placement optimization
+  async optimizePlacement(agents, constraints, objectives) {
+    const results = new Map();
+    
+    // Run multiple algorithms in parallel
+    const algorithmPromises = Object.entries(this.algorithms).map(
+      async ([name, algorithm]) => {
+        const result = await algorithm.optimize(agents, constraints, objectives);
+        return [name, result];
+      }
+    );
+    
+    const algorithmResults = await Promise.all(algorithmPromises);
+    
+    for (const [name, result] of algorithmResults) {
+      results.set(name, result);
+    }
+    
+    // Ensemble optimization - combine best results
+    const ensembleResult = await this.ensembleOptimization(results, objectives);
+    
+    return {
+      bestPlacement: ensembleResult.placement,
+      algorithm: ensembleResult.algorithm,
+      score: ensembleResult.score,
+      individualResults: results,
+      improvementPotential: ensembleResult.improvement
+    };
+  }
+  
+  // Genetic algorithm for agent placement
+  async geneticPlacementOptimization(agents, constraints) {
+    const ga = new GeneticAlgorithm({
+      populationSize: 100,
+      mutationRate: 0.1,
+      crossoverRate: 0.8,
+      maxGenerations: 500,
+      eliteSize: 10
+    });
+    
+    // Initialize population with random placements
+    const initialPopulation = this.generateInitialPlacements(agents, constraints);
+    
+    // Define fitness function
+    const fitnessFunction = (placement) => this.calculatePlacementFitness(placement, constraints);
+    
+    // Evolve optimal placement
+    const result = await ga.evolve(initialPopulation, fitnessFunction);
+    
+    return {
+      placement: result.bestIndividual,
+      fitness: result.bestFitness,
+      generations: result.generations,
+      convergence: result.convergenceHistory
+    };
+  }
+  
+  // Graph partitioning for agent placement
+  async graphPartitioningPlacement(agents, communicationGraph) {
+    // Use METIS-like algorithm for graph partitioning
+    const partitioner = new GraphPartitioner({
+      objective: 'minimize_cut',
+      balanceConstraint: 0.05, // 5% imbalance tolerance
+      refinement: true
+    });
+    
+    // Create communication weight matrix
+    const weights = this.createCommunicationWeights(agents, communicationGraph);
+    
+    // Partition the graph
+    const partitions = await partitioner.partition(communicationGraph, weights);
+    
+    // Map partitions to physical locations
+    const placement = this.mapPartitionsToLocations(partitions, agents);
+    
+    return {
+      placement,
+      partitions,
+      cutWeight: partitioner.getCutWeight(),
+      balance: partitioner.getBalance()
+    };
+  }
+}
+```
+
+### 4. Communication Pattern Optimization
+```javascript
+// Advanced communication pattern optimization
+class CommunicationOptimizer {
+  constructor() {
+    this.patternAnalyzer = new PatternAnalyzer();
+    this.protocolOptimizer = new ProtocolOptimizer();
+    this.messageOptimizer = new MessageOptimizer();
+    this.compressionEngine = new CompressionEngine();
+  }
+  
+  // Comprehensive communication optimization
+  async optimizeCommunication(swarm, historicalData) {
+    // Analyze communication patterns
+    const patterns = await this.patternAnalyzer.analyze(historicalData);
+    
+    // Optimize based on pattern analysis
+    const optimizations = {
+      // Message batching optimization
+      batching: await this.optimizeMessageBatching(patterns),
+      
+      // Protocol selection optimization
+      protocols: await this.optimizeProtocols(patterns),
+      
+      // Compression optimization
+      compression: await this.optimizeCompression(patterns),
+      
+      // Caching strategies
+      caching: await this.optimizeCaching(patterns),
+      
+      // Routing optimization
+      routing: await this.optimizeMessageRouting(patterns)
+    };
+    
+    return optimizations;
+  }
+  
+  // Intelligent message batching
+  async optimizeMessageBatching(patterns) {
+    const batchingStrategies = [
+      new TimeBatchingStrategy(),
+      new SizeBatchingStrategy(),
+      new AdaptiveBatchingStrategy(),
+      new PriorityBatchingStrategy()
+    ];
+    
+    const evaluations = await Promise.all(
+      batchingStrategies.map(strategy => 
+        this.evaluateBatchingStrategy(strategy, patterns)
+      )
+    );
+    
+    const optimal = evaluations.reduce((best, current) => 
+      current.score > best.score ? current : best
+    );
+    
+    return {
+      strategy: optimal.strategy,
+      configuration: optimal.configuration,
+      expectedImprovement: optimal.improvement,
+      metrics: optimal.metrics
+    };
+  }
+  
+  // Dynamic protocol selection
+  async optimizeProtocols(patterns) {
+    const protocols = {
+      tcp: { reliability: 0.99, latency: 'medium', overhead: 'high' },
+      udp: { reliability: 0.95, latency: 'low', overhead: 'low' },
+      websocket: { reliability: 0.98, latency: 'medium', overhead: 'medium' },
+      grpc: { reliability: 0.99, latency: 'low', overhead: 'medium' },
+      mqtt: { reliability: 0.97, latency: 'low', overhead: 'low' }
+    };
+    
+    const recommendations = new Map();
+    
+    for (const [agentPair, pattern] of patterns.pairwisePatterns) {
+      const optimal = this.selectOptimalProtocol(protocols, pattern);
+      recommendations.set(agentPair, optimal);
+    }
+    
+    return recommendations;
+  }
+}
+```
+
+## MCP Integration Hooks
+
+### Topology Management Integration
+```javascript
+// Comprehensive MCP topology integration
+const topologyIntegration = {
+  // Real-time topology optimization
+  async optimizeSwarmTopology(swarmId, optimizationConfig = {}) {
+    // Get current swarm status
+    const swarmStatus = await mcp.swarm_status({ swarmId });
+    
+    // Analyze current topology performance
+    const performance = await mcp.performance_report({ format: 'detailed' });
+    
+    // Identify bottlenecks in current topology
+    const bottlenecks = await mcp.bottleneck_analyze({ component: 'topology' });
+    
+    // Generate optimization recommendations
+    const recommendations = await this.generateTopologyRecommendations(
+      swarmStatus, 
+      performance, 
+      bottlenecks, 
+      optimizationConfig
+    );
+    
+    // Apply optimization if beneficial
+    if (recommendations.beneficial) {
+      const result = await mcp.topology_optimize({ swarmId });
+      
+      // Monitor optimization impact
+      const impact = await this.monitorOptimizationImpact(swarmId, result);
+      
+      return {
+        applied: true,
+        recommendations,
+        result,
+        impact
+      };
+    }
+    
+    return {
+      applied: false,
+      recommendations,
+      reason: 'No beneficial optimization found'
+    };
+  },
+  
+  // Dynamic swarm scaling with topology consideration
+  async scaleWithTopologyOptimization(swarmId, targetSize, workloadProfile) {
+    // Current swarm state
+    const currentState = await mcp.swarm_status({ swarmId });
+    
+    // Calculate optimal topology for target size
+    const optimalTopology = await this.calculateOptimalTopologyForSize(
+      targetSize, 
+      workloadProfile
+    );
+    
+    // Plan scaling strategy
+    const scalingPlan = await this.planTopologyAwareScaling(
+      currentState,
+      targetSize,
+      optimalTopology
+    );
+    
+    // Execute scaling with topology optimization
+    const scalingResult = await mcp.swarm_scale({ 
+      swarmId, 
+      targetSize 
+    });
+    
+    // Apply topology optimization after scaling
+    if (scalingResult.success) {
+      await mcp.topology_optimize({ swarmId });
+    }
+    
+    return {
+      scalingResult,
+      topologyOptimization: scalingResult.success,
+      finalTopology: optimalTopology
+    };
+  },
+  
+  // Coordination optimization
+  async optimizeCoordination(swarmId) {
+    // Analyze coordination patterns
+    const coordinationMetrics = await mcp.coordination_sync({ swarmId });
+    
+    // Identify coordination bottlenecks
+    const coordinationBottlenecks = await mcp.bottleneck_analyze({ 
+      component: 'coordination' 
+    });
+    
+    // Optimize coordination patterns
+    const optimization = await this.optimizeCoordinationPatterns(
+      coordinationMetrics,
+      coordinationBottlenecks
+    );
+    
+    return optimization;
+  }
+};
+```
+
+### Neural Network Integration
+```javascript
+// AI-powered topology optimization
+class NeuralTopologyOptimizer {
+  constructor() {
+    this.models = {
+      topology_predictor: null,
+      performance_estimator: null,
+      pattern_recognizer: null
+    };
+  }
+  
+  // Initialize neural models
+  async initializeModels() {
+    // Load pre-trained models or train new ones
+    this.models.topology_predictor = await mcp.model_load({ 
+      modelPath: '/models/topology_optimizer.model' 
+    });
+    
+    this.models.performance_estimator = await mcp.model_load({ 
+      modelPath: '/models/performance_estimator.model' 
+    });
+    
+    this.models.pattern_recognizer = await mcp.model_load({ 
+      modelPath: '/models/pattern_recognizer.model' 
+    });
+  }
+  
+  // AI-powered topology prediction
+  async predictOptimalTopology(swarmState, workloadProfile) {
+    if (!this.models.topology_predictor) {
+      await this.initializeModels();
+    }
+    
+    // Prepare input features
+    const features = this.extractTopologyFeatures(swarmState, workloadProfile);
+    
+    // Predict optimal topology
+    const prediction = await mcp.neural_predict({
+      modelId: this.models.topology_predictor.id,
+      input: JSON.stringify(features)
+    });
+    
+    return {
+      predictedTopology: prediction.topology,
+      confidence: prediction.confidence,
+      expectedImprovement: prediction.improvement,
+      reasoning: prediction.reasoning
+    };
+  }
+  
+  // Train topology optimization model
+  async trainTopologyModel(trainingData) {
+    const trainingConfig = {
+      pattern_type: 'optimization',
+      training_data: JSON.stringify(trainingData),
+      epochs: 100
+    };
+    
+    const trainingResult = await mcp.neural_train(trainingConfig);
+    
+    // Save trained model
+    if (trainingResult.success) {
+      await mcp.model_save({
+        modelId: trainingResult.modelId,
+        path: '/models/topology_optimizer.model'
+      });
+    }
+    
+    return trainingResult;
+  }
+}
+```
+
+## Advanced Optimization Algorithms
+
+### 1. Genetic Algorithm for Topology Evolution
+```javascript
+// Genetic algorithm implementation for topology optimization
+class GeneticTopologyOptimizer {
+  constructor(config = {}) {
+    this.populationSize = config.populationSize || 50;
+    this.mutationRate = config.mutationRate || 0.1;
+    this.crossoverRate = config.crossoverRate || 0.8;
+    this.maxGenerations = config.maxGenerations || 100;
+    this.eliteSize = config.eliteSize || 5;
+  }
+  
+  // Evolve optimal topology
+  async evolve(initialTopologies, fitnessFunction, constraints) {
+    let population = initialTopologies;
+    let generation = 0;
+    let bestFitness = -Infinity;
+    let bestTopology = null;
+    
+    const convergenceHistory = [];
+    
+    while (generation < this.maxGenerations) {
+      // Evaluate fitness for each topology
+      const fitness = await Promise.all(
+        population.map(topology => fitnessFunction(topology, constraints))
+      );
+      
+      // Track best solution
+      const maxFitnessIndex = fitness.indexOf(Math.max(...fitness));
+      if (fitness[maxFitnessIndex] > bestFitness) {
+        bestFitness = fitness[maxFitnessIndex];
+        bestTopology = population[maxFitnessIndex];
+      }
+      
+      convergenceHistory.push({
+        generation,
+        bestFitness,
+        averageFitness: fitness.reduce((a, b) => a + b) / fitness.length
+      });
+      
+      // Selection
+      const selected = this.selection(population, fitness);
+      
+      // Crossover
+      const offspring = await this.crossover(selected);
+      
+      // Mutation
+      const mutated = await this.mutation(offspring, constraints);
+      
+      // Next generation
+      population = this.nextGeneration(population, fitness, mutated);
+      generation++;
+    }
+    
+    return {
+      bestTopology,
+      bestFitness,
+      generation,
+      convergenceHistory
+    };
+  }
+  
+  // Topology crossover operation
+  async crossover(parents) {
+    const offspring = [];
+    
+    for (let i = 0; i < parents.length - 1; i += 2) {
+      if (Math.random() < this.crossoverRate) {
+        const [child1, child2] = await this.crossoverTopologies(
+          parents[i], 
+          parents[i + 1]
+        );
+        offspring.push(child1, child2);
+      } else {
+        offspring.push(parents[i], parents[i + 1]);
+      }
+    }
+    
+    return offspring;
+  }
+  
+  // Topology mutation operation
+  async mutation(population, constraints) {
+    return Promise.all(
+      population.map(async topology => {
+        if (Math.random() < this.mutationRate) {
+          return await this.mutateTopology(topology, constraints);
+        }
+        return topology;
+      })
+    );
+  }
+}
+```
+
+### 2. Simulated Annealing for Topology Optimization
+```javascript
+// Simulated annealing implementation
+class SimulatedAnnealingOptimizer {
+  constructor(config = {}) {
+    this.initialTemperature = config.initialTemperature || 1000;
+    this.coolingRate = config.coolingRate || 0.95;
+    this.minTemperature = config.minTemperature || 1;
+    this.maxIterations = config.maxIterations || 10000;
+  }
+  
+  // Simulated annealing optimization
+  async optimize(initialTopology, objectiveFunction, constraints) {
+    let currentTopology = initialTopology;
+    let currentScore = await objectiveFunction(currentTopology, constraints);
+    
+    let bestTopology = currentTopology;
+    let bestScore = currentScore;
+    
+    let temperature = this.initialTemperature;
+    let iteration = 0;
+    
+    const history = [];
+    
+    while (temperature > this.minTemperature && iteration < this.maxIterations) {
+      // Generate neighbor topology
+      const neighborTopology = await this.generateNeighbor(currentTopology, constraints);
+      const neighborScore = await objectiveFunction(neighborTopology, constraints);
+      
+      // Accept or reject the neighbor
+      const deltaScore = neighborScore - currentScore;
+      
+      if (deltaScore > 0 || Math.random() < Math.exp(deltaScore / temperature)) {
+        currentTopology = neighborTopology;
+        currentScore = neighborScore;
+        
+        // Update best solution
+        if (neighborScore > bestScore) {
+          bestTopology = neighborTopology;
+          bestScore = neighborScore;
+        }
+      }
+      
+      // Record history
+      history.push({
+        iteration,
+        temperature,
+        currentScore,
+        bestScore
+      });
+      
+      // Cool down
+      temperature *= this.coolingRate;
+      iteration++;
+    }
+    
+    return {
+      bestTopology,
+      bestScore,
+      iterations: iteration,
+      history
+    };
+  }
+  
+  // Generate neighbor topology through local modifications
+  async generateNeighbor(topology, constraints) {
+    const modifications = [
+      () => this.addConnection(topology, constraints),
+      () => this.removeConnection(topology, constraints),
+      () => this.modifyConnection(topology, constraints),
+      () => this.relocateAgent(topology, constraints)
+    ];
+    
+    const modification = modifications[Math.floor(Math.random() * modifications.length)];
+    return await modification();
+  }
+}
+```
+
+## Operational Commands
+
+### Topology Optimization Commands
+```bash
+# Analyze current topology
+npx claude-flow topology-analyze --swarm-id <id> --metrics performance
+
+# Optimize topology automatically
+npx claude-flow topology-optimize --swarm-id <id> --strategy adaptive
+
+# Compare topology configurations
+npx claude-flow topology-compare --topologies ["hierarchical", "mesh", "hybrid"]
+
+# Generate topology recommendations
+npx claude-flow topology-recommend --workload-profile <file> --constraints <file>
+
+# Monitor topology performance
+npx claude-flow topology-monitor --swarm-id <id> --interval 60
+```
+
+### Agent Placement Commands
+```bash
+# Optimize agent placement
+npx claude-flow placement-optimize --algorithm genetic --agents <agent-list>
+
+# Analyze placement efficiency
+npx claude-flow placement-analyze --current-placement <config>
+
+# Generate placement recommendations
+npx claude-flow placement-recommend --communication-patterns <file>
+```
+
+## Integration Points
+
+### With Other Optimization Agents
+- **Load Balancer**: Coordinates topology changes with load distribution
+- **Performance Monitor**: Receives topology performance metrics
+- **Resource Manager**: Considers resource constraints in topology decisions
+
+### With Swarm Infrastructure
+- **Task Orchestrator**: Adapts task distribution to topology changes
+- **Agent Coordinator**: Manages agent connections during topology updates
+- **Memory System**: Stores topology optimization history and patterns
+
+## Performance Metrics
+
+### Topology Performance Indicators
+```javascript
+// Comprehensive topology metrics
+const topologyMetrics = {
+  // Communication efficiency
+  communicationEfficiency: {
+    latency: this.calculateAverageLatency(),
+    throughput: this.calculateThroughput(),
+    bandwidth_utilization: this.calculateBandwidthUtilization(),
+    message_overhead: this.calculateMessageOverhead()
+  },
+  
+  // Network topology metrics
+  networkMetrics: {
+    diameter: this.calculateNetworkDiameter(),
+    clustering_coefficient: this.calculateClusteringCoefficient(),
+    betweenness_centrality: this.calculateBetweennessCentrality(),
+    degree_distribution: this.calculateDegreeDistribution()
+  },
+  
+  // Fault tolerance
+  faultTolerance: {
+    connectivity: this.calculateConnectivity(),
+    redundancy: this.calculateRedundancy(),
+    single_point_failures: this.identifySinglePointFailures(),
+    recovery_time: this.calculateRecoveryTime()
+  },
+  
+  // Scalability metrics
+  scalability: {
+    growth_capacity: this.calculateGrowthCapacity(),
+    scaling_efficiency: this.calculateScalingEfficiency(),
+    bottleneck_points: this.identifyBottleneckPoints(),
+    optimal_size: this.calculateOptimalSize()
+  }
+};
+```
+
+This Topology Optimizer agent provides sophisticated swarm topology optimization with AI-powered decision making, advanced algorithms, and comprehensive performance monitoring for optimal swarm coordination.