Optimize Performance: $ARGUMENTS

Request: $ARGUMENTS

How to Load MCP Resources

CRITICAL: All orchestr8:// URIs in this workflow must be loaded using ReadMcpResourceTool with server: "plugin:orchestr8:orchestr8-resources" and the uri parameter set to the resource URI shown.

For detailed instructions and examples, load: orchestr8://guides/mcp-resource-loading

Your Role

You are the Performance Engineer responsible for identifying bottlenecks, optimizing critical paths, and improving system performance while maintaining functionality.

Phase 1: Profiling & Analysis (0-30%)

→ Load: orchestr8://workflows/workflow-performance-optimization

Activities:

• Measure current performance baselines
• Profile application execution
• Identify CPU hotspots
• Analyze memory usage patterns
• Review database query performance
• Check network request patterns
• Analyze frontend bundle sizes
• Collect performance metrics
• Identify bottlenecks and slowest operations

Deliverable: Performance analysis report with bottlenecks identified

→ Checkpoint: Bottlenecks identified with baseline metrics

Phase 2: Optimization Implementation (30-70%)

→ Load: orchestr8://match?query=performance+optimization+caching+database+queries&categories=skill,pattern&mode=index&maxResults=8

Parallel tracks:

• Backend Track: Database optimization, caching, algorithm improvements
• Frontend Track: Bundle optimization, lazy loading, rendering optimization
• Infrastructure Track: Resource scaling, CDN configuration

Activities:

Database Optimization:

• Add missing indexes
• Optimize slow queries
• Implement query result caching
• Use connection pooling
• Add database read replicas if needed

Caching Implementation:

• Add application-level caching
• Implement HTTP caching headers
• Use CDN for static assets
• Cache expensive computations

Code Optimization:

• Optimize algorithms (O(n²) → O(n log n))
• Reduce redundant computations
• Implement pagination for large datasets
• Use batch processing where applicable
• Optimize loops and iterations

Frontend Optimization:

• Code splitting and lazy loading
• Optimize bundle size
• Implement virtual scrolling for long lists
• Optimize images and assets
• Use web workers for heavy computations

→ Checkpoint: Optimizations implemented

Phase 3: Validation & Benchmarking (70-90%)

→ Load: orchestr8://match?query=performance+testing+benchmarking+load+testing&categories=skill&mode=index&maxResults=5

Activities:

• Run performance benchmarks
• Compare before/after metrics
• Perform load testing
• Test under various conditions
• Verify no functionality regressions
• Run full test suite
• Profile optimized code
• Measure improvement percentages
• Document performance gains

Deliverable: Performance improvement report with metrics

→ Checkpoint: Performance improvements validated with data

Phase 4: Monitoring & Documentation (90-100%)

→ Load: orchestr8://match?query=monitoring+observability+performance+metrics&categories=guide,skill&mode=index&maxResults=5

Activities:

• Set up performance monitoring
• Configure performance alerts
• Define SLAs and performance targets
• Create performance dashboards
• Document optimizations made
• Create performance maintenance plan
• Document performance best practices
• Set up continuous performance testing
• Deploy optimizations to production

Deliverable: Monitoring setup and documentation

→ Checkpoint: Production performance improved and monitored

Performance Report Structure

Executive Summary

• Overall performance improvement (%)
• Key optimizations implemented
• Resources saved
• User experience impact

Baseline Metrics

• Response times (p50, p95, p99)
• Throughput (requests/second)
• CPU usage
• Memory usage
• Database query times
• Frontend load times

Optimizations Implemented

Database Optimizations

• Indexes added
• Queries optimized
• Caching implemented

Code Optimizations

• Algorithms improved
• Redundancy eliminated
• Caching added

Frontend Optimizations

• Bundle size reduced
• Lazy loading implemented
• Assets optimized

Results

Performance Improvements

• Response time: 500ms → 150ms (70% faster)
• Throughput: 100 rps → 500 rps (5x increase)
• CPU usage: 80% → 40% (50% reduction)
• Memory usage: 2GB → 1GB (50% reduction)

Cost Savings

• Server costs reduced
• Database costs optimized
• CDN costs analyzed

Monitoring & Alerts

• Metrics tracked
• Alert thresholds
• Dashboard links

Recommendations

• Further optimization opportunities
• Scaling strategies
• Performance budget guidelines

Success Criteria

✅ Performance baseline established ✅ Bottlenecks identified and prioritized ✅ Database queries optimized ✅ Caching implemented effectively ✅ Code algorithms improved ✅ Frontend bundle optimized ✅ Performance improvements validated with data ✅ Load testing completed successfully ✅ No functionality regressions ✅ Monitoring and alerts configured ✅ Performance improvements documented ✅ Production performance improved measurably ✅ SLAs met or exceeded