AG

agent-performance-monitor

Collects real-time metrics and analyzes bottlenecks to maintain agent SLA and system performance.

Install

mkdir -p .claude/skills/agent-performance-monitor && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/815" && unzip -o skill.zip -d .claude/skills/agent-performance-monitor && rm skill.zip

Installs to .claude/skills/agent-performance-monitor

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Agent skill for performance-monitor - invoke with $agent-performance-monitor
76 charsno explicit “when” trigger
Advanced

Key capabilities

  • Collect real-time system and agent metrics
  • Detect performance bottlenecks across layers
  • Monitor SLA compliance for services
  • Track resource utilization and efficiency
  • Analyze recurring performance patterns

How it works

The agent collects multi-dimensional metrics and uses intelligent detectors to correlate bottlenecks and evaluate SLA violations.

Inputs & outputs

You give it
Monitoring command or service name
You get back
Real-time performance report and alerts

When to use agent-performance-monitor

  • Track agent CPU and memory usage
  • Analyze system load averages
  • Monitor SLA compliance
  • Detect performance anomalies

About this skill


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();
  }
  
  // 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

// Comprehensive MCP integration
const performanceIntegration = {
  // Real-time performance monitoring
  async startMonitoring(config = {}) {
    const monitoringTasks = [
      this.monitorSwarmHealth(),
 

---

*Content truncated.*

When not to use it

  • Monitoring non-agent-based infrastructure
  • Deep packet inspection for network security

Prerequisites

Access to swarm infrastructurePerformance monitor agent configuration

Limitations

  • Requires integration with swarm infrastructure
  • Pattern recognition accuracy depends on historical data

How it compares

It provides agent-specific performance insights and automated bottleneck prioritization instead of generic system monitoring.

Compared to similar skills

agent-performance-monitor side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
agent-performance-monitor (this skill)36moReviewAdvanced
service-mesh-observability52moNo flagsAdvanced
observability-engineer124moNo flagsAdvanced
gcloud-usage17moNo flagsIntermediate

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