lodetomasi Performance Monitor
Overview
Tracks and analyzes performance metrics for Claude Code skills and tools, recording execution time, memory usage, success/failure rates, and generating comprehensive performance reports.
When to Use
- After implementing new skills to establish baselines
- When investigating performance degradation
- For capacity planning and resource optimization
- During performance optimization cycles
- When debugging memory issues
Capabilities
1. Metrics Collection
- Execution time tracking
- Memory usage monitoring (MB)
- Success/failure rate recording
- Timestamp logging
- Error message capture
2. Statistical Analysis
- Average/min/max execution times
- Memory usage patterns
- Success rate calculations
- Usage frequency tracking
- Trend analysis
3. Performance Reporting
- Summary reports by skill
- Top slowest skills identification
- Memory-intensive skill detection
- Most-used skills ranking
- Failure rate analysis
Usage
Generate Performance Report
python .claude/skills/performance-monitor/performance-monitor.py report
Run Test Execution with Monitoring
python .claude/skills/performance-monitor/performance-monitor.py test
Custom Metrics File
python .claude/skills/performance-monitor/performance-monitor.py report custom_metrics.json
Report Output Example
================================================================================
PERFORMANCE MONITORING REPORT
================================================================================
Total recorded executions: 147
Tracked skills: 12
Skill Executions Avg Time Avg Memory Success Rate
--------------------------------------------------------------------------------
database-optimizer 45 2.341s 124.5MB 100.0%
python-profiler 32 1.892s 89.3MB 96.9%
sast-analyzer 28 5.123s 256.7MB 100.0%
aws-cost-analyzer 18 3.445s 178.2MB 94.4%
memory-analyzer 12 4.567s 312.1MB 91.7%
code-reviewer 8 1.234s 67.8MB 100.0%
================================================================================
PERFORMANCE INSIGHTS
================================================================================
🐌 Slowest Skills (Average):
• sast-analyzer: 5.123s
• memory-analyzer: 4.567s
• aws-cost-analyzer: 3.445s
💾 Most Memory Intensive:
• memory-analyzer: 312.1MB
• sast-analyzer: 256.7MB
• aws-cost-analyzer: 178.2MB
⭐ Most Used Skills:
• database-optimizer: 45 executions
• python-profiler: 32 executions
• sast-analyzer: 28 executions
⚠️ Skills with Failures:
• memory-analyzer: 1 failures (8.3%)
• aws-cost-analyzer: 1 failures (5.6%)
• python-profiler: 1 failures (3.1%)
Instrumenting Your Code
Python Decorator
from .claude.skills.performance-monitor import performance_monitor
@monitor_performance('my-custom-skill')
def my_skill_function():
# Your code here
pass
Manual Recording
from .claude.skills.performance-monitor import PerformanceMonitor
import time
monitor = PerformanceMonitor()
start = time.time()
try:
# Your code
success = True
error = None
except Exception as e:
success = False
error = str(e)
execution_time = time.time() - start
memory_used = 50 # MB
monitor.record_execution(
skill_name='my-skill',
execution_time=execution_time,
memory_used=memory_used,
success=success,
error=error
)
Data Storage
Metrics are stored in JSON format at performance_metrics.json:
[
{
"timestamp": "2025-01-15T10:30:45.123456",
"skill": "python-profiler",
"execution_time": 1.892,
"memory_used_mb": 89.3,
"success": true,
"error": null
}
]
Performance Optimization Guidelines
Execution Time
- Target: < 5s for most skills
- Acceptable: 5-10s for complex analysis
- Investigate: > 10s execution time
Memory Usage
- Light: < 100MB
- Moderate: 100-300MB
- Heavy: > 300MB (review for optimization)
Success Rate
- Excellent: > 98%
- Good: 95-98%
- Needs attention: < 95%
Troubleshooting
High Execution Time
- Profile the skill with cProfile or pyinstrument
- Check for inefficient algorithms (O(n²))
- Look for unnecessary I/O operations
- Review external API calls
High Memory Usage
- Check for memory leaks
- Review large data structure usage
- Use generators instead of lists
- Implement streaming for large files
Low Success Rate
- Review error messages in metrics file
- Add error handling
- Validate input data
- Add retry logic for transient failures
CI/CD Integration
GitHub Actions
name: Performance Monitoring
on: [push]
jobs:
monitor:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Run with monitoring
run: |
python .claude/skills/performance-monitor/performance-monitor.py test
- name: Generate report
run: |
python .claude/skills/performance-monitor/performance-monitor.py report
Requirements
pip install psutil # For memory monitoring
Metrics Retention
- Keep last 1000 executions per skill
- Archive monthly reports
- Clear old metrics:
rm performance_metrics.json