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commands/workflows/performance-optimization.md
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commands/workflows/performance-optimization.md
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model: claude-opus-4-1
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Optimize application performance end-to-end using specialized performance and optimization agents:
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[Extended thinking: This workflow coordinates multiple agents to identify and fix performance bottlenecks across the entire stack. From database queries to frontend rendering, each agent contributes their expertise to create a highly optimized application.]
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## Phase 1: Performance Analysis
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### 1. Application Profiling
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- Use Task tool with subagent_type="performance-engineer"
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- Prompt: "Profile application performance for: $ARGUMENTS. Identify CPU, memory, and I/O bottlenecks. Include flame graphs, memory profiles, and resource utilization metrics."
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- Output: Performance profile, bottleneck analysis, optimization priorities
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### 2. Database Performance Analysis
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- Use Task tool with subagent_type="database-optimizer"
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- Prompt: "Analyze database performance for: $ARGUMENTS. Review query execution plans, identify slow queries, check indexing, and analyze connection pooling."
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- Output: Query optimization report, index recommendations, schema improvements
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## Phase 2: Backend Optimization
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### 3. Backend Code Optimization
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- Use Task tool with subagent_type="performance-engineer"
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- Prompt: "Optimize backend code for: $ARGUMENTS based on profiling results. Focus on algorithm efficiency, caching strategies, and async operations."
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- Output: Optimized code, caching implementation, performance improvements
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### 4. API Optimization
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- Use Task tool with subagent_type="backend-architect"
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- Prompt: "Optimize API design and implementation for: $ARGUMENTS. Consider pagination, response compression, field filtering, and batch operations."
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- Output: Optimized API endpoints, GraphQL query optimization, response time improvements
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## Phase 3: Frontend Optimization
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### 5. Frontend Performance
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- Use Task tool with subagent_type="frontend-developer"
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- Prompt: "Optimize frontend performance for: $ARGUMENTS. Focus on bundle size, lazy loading, code splitting, and rendering performance. Implement Core Web Vitals improvements."
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- Output: Optimized bundles, lazy loading implementation, performance metrics
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### 6. Mobile App Optimization
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- Use Task tool with subagent_type="mobile-developer"
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- Prompt: "Optimize mobile app performance for: $ARGUMENTS. Focus on startup time, memory usage, battery efficiency, and offline performance."
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- Output: Optimized mobile code, reduced app size, improved battery life
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## Phase 4: Infrastructure Optimization
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### 7. Cloud Infrastructure Optimization
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- Use Task tool with subagent_type="cloud-architect"
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- Prompt: "Optimize cloud infrastructure for: $ARGUMENTS. Review auto-scaling, instance types, CDN usage, and geographic distribution."
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- Output: Infrastructure improvements, cost optimization, scaling strategy
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### 8. Deployment Optimization
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- Use Task tool with subagent_type="deployment-engineer"
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- Prompt: "Optimize deployment and build processes for: $ARGUMENTS. Improve CI/CD performance, implement caching, and optimize container images."
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- Output: Faster builds, optimized containers, improved deployment times
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## Phase 5: Monitoring and Validation
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### 9. Performance Monitoring Setup
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- Use Task tool with subagent_type="devops-troubleshooter"
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- Prompt: "Set up comprehensive performance monitoring for: $ARGUMENTS. Include APM, real user monitoring, and custom performance metrics."
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- Output: Monitoring dashboards, alert thresholds, SLO definitions
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### 10. Performance Testing
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- Use Task tool with subagent_type="test-automator"
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- Prompt: "Create performance test suites for: $ARGUMENTS. Include load tests, stress tests, and performance regression tests."
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- Output: Performance test suite, benchmark results, regression prevention
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## Coordination Notes
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- Performance metrics guide optimization priorities
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- Each optimization must be validated with measurements
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- Consider trade-offs between different performance aspects
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- Document all optimizations and their impact
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Performance optimization target: $ARGUMENTS
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