Performance Monitor Skill

Overview

This skill provides comprehensive real-time performance monitoring capabilities including metrics collection, bottleneck detection, SLA compliance tracking, anomaly detection, and resource utilization monitoring for swarm-based systems.

When to Use

Continuous monitoring of swarm performance
Detecting performance bottlenecks before they impact operations
Tracking SLA compliance and generating alerts
Anomaly detection in system metrics
Resource utilization tracking and forecasting
Building real-time performance dashboards

Quick Start

# 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"]

Architecture

+-----------------------------------------------------------+
|                  Performance Monitor                       |
+-----------------------------------------------------------+
|  Metrics Collector  |  Bottleneck Analyzer  |  SLA Monitor |
+---------------------+-----------------------+--------------+
         |                     |                      |
         v                     v                      v
+-------------------+  +------------------+  +---------------+
| System Metrics    |  | Pattern Detection|  | Threshold     |
| - CPU/Memory      |  | - CPU Bottleneck |  | Checking      |
| - I/O/Network     |  | - Memory Leak    |  | - Availability|
| - Process Stats   |  | - I/O Saturation |  | - Response    |
+-------------------+  | - Network Issues |  | - Throughput  |
                       +------------------+  +---------------+
         |                     |                      |
         v                     v                      v
+-----------------------------------------------------------+
|              Dashboard Provider (Real-time)                |
+-----------------------------------------------------------+

Core Capabilities

1. Multi-Dimensional Metrics Collection

// Real-time metrics collection
const metrics = await mcp.metrics_collect({
  components: ['cpu', 'memory', 'network', 'agents']
});

// System metrics include:
// - CPU: usage, load average, core utilization
// - Memory: usage, available, pressure
// - I/O: disk usage, disk I/O, network I/O
// - Processes: count, threads, handles

2. Bottleneck Detection

Detects and categorizes bottlenecks:

CPU Bottlenecks: High CPU usage, core saturation
Memory Bottlenecks: Memory pressure, leak detection
I/O Bottlenecks: Disk saturation, network congestion
Coordination Bottlenecks: Agent communication delays
Task Queue Bottlenecks: Queue backup, processing delays

# Analyze specific component
npx claude-flow bottleneck-analyze --component task-queue

# Full system analysis
npx claude-flow bottleneck-analyze

3. SLA Monitoring

Configure and monitor SLA metrics:

| Metric | Description | Typical Threshold | |--------|-------------|-------------------| | Availability | System uptime percentage | 99.9% | | Response Time | Request latency | < 1000ms | | Throughput | Requests per second | > 100 RPS | | Error Rate | Failed requests percentage | < 0.1% | | Recovery Time | Time to recover from failure | < 300s |

4. Anomaly Detection

Multi-model anomaly detection:

Statistical: 3-sigma rule deviation detection
Machine Learning: Trained anomaly detection models
Time Series: LSTM-based temporal anomaly detection
Behavioral: Agent behavior pattern analysis

Key Metrics

KPIs Monitored

| Category | Metrics | |----------|---------| | Availability | Uptime, MTBF, MTTR | | Performance | Response time (p50/p90/p95/p99), throughput | | Efficiency | Resource utilization, cost per transaction | | Reliability | Error rate, success rate, fault tolerance |

Resource Tracking

CPU: Current, peak, average utilization with percentiles
Memory: Usage trends, leak detection, pressure indicators
Network: Bandwidth utilization, latency, packet loss
Agents: Per-agent efficiency, responsiveness, reliability

MCP Integration

// Comprehensive monitoring setup
const monitoring = {
  // Start all monitors
  async startMonitoring() {
    const [health, performance, bottlenecks] = await Promise.all([
      mcp.health_check({ components: ['swarm', 'coordination'] }),
      mcp.performance_report({ format: 'detailed', timeframe: '24h' }),
      mcp.bottleneck_analyze({})
    ]);

    return { health, performance, bottlenecks };
  },

  // Agent performance tracking
  async monitorAgents(swarmId) {
    const agents = await mcp.agent_list({ swarmId });
    const metrics = new Map();

    for (const agent of agents) {
      metrics.set(agent.id, await mcp.agent_metrics({ agentId: agent.id }));
    }

    return metrics;
  }
};

Alert Configuration

# 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

| Integration | Purpose | |-------------|---------| | Load Balancer | Provides performance data for load balancing decisions | | Topology Optimizer | Supplies network and coordination metrics | | Resource Allocator | Shares resource utilization and forecasting data | | Task Orchestrator | Monitors task execution performance |

Best Practices

Baseline Establishment: Collect baseline metrics before monitoring for anomalies
Alert Tuning: Start with conservative thresholds, tune based on false positive rate
Multi-Layer Monitoring: Monitor at system, agent, and task levels
Historical Analysis: Retain metrics for trend analysis and capacity planning
Proactive Detection: Use predictive analytics to detect issues before impact

Example: Dashboard Data Provider

// Real-time dashboard data
const dashboardData = {
  overview: {
    swarmHealth: 'healthy',
    activeAgents: 12,
    totalTasks: 1547,
    averageResponseTime: 45  // ms
  },
  performance: {
    throughput: 250,  // tasks/sec
    latency: { p50: 40, p90: 85, p99: 120 },  // ms
    errorRate: 0.02,  // percentage
    utilization: 0.72  // percentage
  },
  alerts: [],
  timestamp: Date.now()
};

Related Skills

optimization-benchmark - Comprehensive performance benchmarking
optimization-load-balancer - Dynamic load distribution
optimization-resources - Resource allocation and scaling
optimization-topology - Network topology optimization

Version History

1.0.0 (2026-01-02): Initial release - converted from performance-monitor agent with metrics collection, bottleneck detection, SLA monitoring, anomaly detection, and dashboard integration