performance-analysis

Performance Analysis Skill

Comprehensive performance analysis suite for identifying bottlenecks, profiling swarm operations, generating detailed reports, and providing actionable optimization recommendations.

Overview

This skill consolidates all performance analysis capabilities:

• Bottleneck Detection: Identify performance bottlenecks across communication, processing, memory, and network
• Performance Profiling: Real-time monitoring and historical analysis of swarm operations
• Report Generation: Create comprehensive performance reports in multiple formats
• Optimization Recommendations: AI-powered suggestions for improving performance

Quick Start

Basic Bottleneck Detection

npx claude-flow bottleneck detect

Generate Performance Report

npx claude-flow analysis performance-report --format html --include-metrics

Analyze and Auto-Fix

npx claude-flow bottleneck detect --fix --threshold 15

Core Capabilities

1. Bottleneck Detection

Command Syntax

npx claude-flow bottleneck detect [options]

Options

• --swarm-id, -s <id> - Analyze specific swarm (default: current)
• --time-range, -t <range> - Analysis period: 1h, 24h, 7d, all (default: 1h)
• --threshold <percent> - Bottleneck threshold percentage (default: 20)
• --export, -e <file> - Export analysis to file
• --fix - Apply automatic optimizations

Usage Examples

# Basic detection for current swarm
npx claude-flow bottleneck detect

# Analyze specific swarm over 24 hours
npx claude-flow bottleneck detect --swarm-id swarm-123 -t 24h

# Export detailed analysis
npx claude-flow bottleneck detect -t 24h -e bottlenecks.json

# Auto-fix detected issues
npx claude-flow bottleneck detect --fix --threshold 15

# Low threshold for sensitive detection
npx claude-flow bottleneck detect --threshold 10 --export critical-issues.json

Metrics Analyzed

Communication Bottlenecks:

• Message queue delays
• Agent response times
• Coordination overhead
• Memory access patterns
• Inter-agent communication latency

Processing Bottlenecks:

• Task completion times
• Agent utilization rates
• Parallel execution efficiency
• Resource contention
• CPU/memory usage patterns

Memory Bottlenecks:

• Cache hit rates
• Memory access patterns
• Storage I/O performance
• Neural pattern loading times
• Memory allocation efficiency

Network Bottlenecks:

• API call latency
• MCP communication delays
• External service timeouts
• Concurrent request limits
• Network throughput issues

Output Format

🔍 Bottleneck Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━

📊 Summary
├── Time Range: Last 1 hour
├── Agents Analyzed: 6
├── Tasks Processed: 42
└── Critical Issues: 2

🚨 Critical Bottlenecks
1. Agent Communication (35% impact)
   └── coordinator → coder-1 messages delayed by 2.3s avg

2. Memory Access (28% impact)
   └── Neural pattern loading taking 1.8s per access

⚠️ Warning Bottlenecks
1. Task Queue (18% impact)
   └── 5 tasks waiting > 10s for assignment

💡 Recommendations
1. Switch to hierarchical topology (est. 40% improvement)
2. Enable memory caching (est. 25% improvement)
3. Increase agent concurrency to 8 (est. 20% improvement)

✅ Quick Fixes Available
Run with --fix to apply:
- Enable smart caching
- Optimize message routing
- Adjust agent priorities

2. Performance Profiling

Real-time Detection

Automatic analysis during task execution:

• Execution time vs. complexity
• Agent utilization rates
• Resource constraints
• Operation patterns

Common Bottleneck Patterns

Time Bottlenecks:

• Tasks taking > 5 minutes
• Sequential operations that could parallelize
• Redundant file operations
• Inefficient algorithm implementations

Coordination Bottlenecks:

• Single agent for complex tasks
• Unbalanced agent workloads
• Poor topology selection
• Excessive synchronization points

Resource Bottlenecks:

• High operation count (> 100)
• Memory constraints
• I/O limitations
• Thread pool saturation

MCP Integration

// Check for bottlenecks in Claude Code
mcp__claude-flow__bottleneck_detect({
  timeRange: "1h",
  threshold: 20,
  autoFix: false
})

// Get detailed task results with bottleneck analysis
mcp__claude-flow__task_results({
  taskId: "task-123",
  format: "detailed"
})

Result Format:

{
  "bottlenecks": [
    {
      "type": "coordination",
      "severity": "high",
      "description": "Single agent used for complex task",
      "recommendation": "Spawn specialized agents for parallel work",
      "impact": "35%",
      "affectedComponents": ["coordinator", "coder-1"]
    }
  ],
  "improvements": [
    {
      "area": "execution_time",
      "suggestion": "Use parallel task execution",
      "expectedImprovement": "30-50% time reduction",
      "implementationSteps": [
        "Split task into smaller units",
        "Spawn 3-4 specialized agents",
        "Use mesh topology for coordination"
      ]
    }
  ],
  "metrics": {
    "avgExecutionTime": "142s",
    "agentUtilization": "67%",
    "cacheHitRate": "82%",
    "parallelizationFactor": 1.2
  }
}

3. Report Generation

Command Syntax

npx claude-flow analysis performance-report [options]

Options

• --format <type> - Report format: json, html, markdown (default: markdown)
• --include-metrics - Include detailed metrics and charts
• --compare <id> - Compare with previous swarm
• --time-range <range> - Analysis period: 1h, 24h, 7d, 30d, all
• --output <file> - Output file path
• --sections <list> - Comma-separated sections to include

Report Sections

1. Executive Summary
2. Overall performance score
3. Key metrics overview
4. Critical findings

1. Swarm Overview
2. Topology configuration
3. Agent distribution
4. Task statistics

1. Performance Metrics
2. Execution times
3. Throughput analysis
4. Resource utilization
5. Latency breakdown

1. Bottleneck Analysis
2. Identified bottlenecks
3. Impact assessment
4. Optimization priorities

1. Comparative Analysis (when --compare used)
2. Performance trends
3. Improvement metrics
4. Regression detection

1. Recommendations
2. Prioritized action items
3. Expected improvements
4. Implementation guidance

Usage Examples

# Generate HTML report with all metrics
npx claude-flow analysis performance-report --format html --include-metrics

# Compare current swarm with previous
npx claude-flow analysis performance-report --compare swarm-123 --format markdown

# Custom output with specific sections
npx claude-flow analysis performance-report \
  --sections summary,metrics,recommendations \
  --output reports/perf-analysis.html \
  --format html

# Weekly performance report
npx claude-flow analysis performance-report \
  --time-range 7d \
  --include-metrics \
  --format markdown \
  --output docs/weekly-performance.md

# JSON format for CI/CD integration
npx claude-flow analysis performance-report \
  --format json \
  --output build/performance.json

Sample Markdown Report

# Performance Analysis Report

## Executive Summary
- **Overall Score**: 87/100
- **Analysis Period**: Last 24 hours
- **Swarms Analyzed**: 3
- **Critical Issues**: 1

## Key Metrics
| Metric | Value | Trend | Target |
|--------|-------|-------|--------|
| Avg Task Time | 42s | ↓ 12% | 35s |
| Agent Utilization | 78% | ↑ 5% | 85% |
| Cache Hit Rate | 91% | → | 90% |
| Parallel Efficiency | 2.3x | ↑ 0.4x | 2.5x |

## Bottleneck Analysis
### Critical
1. **Agent Communication Delay** (Impact: 35%)
   - Coordinator → Coder messages delayed by 2.3s avg
   - **Fix**: Switch to hierarchical topology

### Warnings
1. **Memory Access Pattern** (Impact: 18%)
   - Neural pattern loading: 1.8s per access
   - **Fix**: Enable memory caching

## Recommendations
1. **High Priority**: Switch to hierarchical topology (40% improvement)