ruvnet/ruflo1 file

Agent Performance Monitor

Agent skill for performance-monitor - invoke with $agent-performance-monitor

Specification
Skill ID
ruvnet/ruflo/agent-performance-monitor
Publisher
ruvnet
Repository
ruflo
Installs
0
Files
1
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

ruvnet/ruflo/agent-performance-monitorInstalls these files
  • SKILL.md

What this skill tells the agent


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

// 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

// 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

// 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

// 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();
  }
  

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