[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C2RZAXAVRDV9B9M3HE9M33":3,"skill-same:01M6C2RZAXAVRDV9B9M3HE9M33":93},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":69,"ownerHtmlUrl":70,"ownerBlogUrl":71,"ownerDescription":72,"updatedAt":73,"install":74,"resources":86},"01M6C2RZAXAVRDV9B9M3HE9M33","flow-performance-optimization",null,"Orchestrate continuous performance optimization with baseline establishment, bottleneck identification, optimization implementation, load testing, and SLO validation","jmagly\u002Faiwg","jmagly","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1159087?v=4","01M6C10AK8N2WCSQN11FYGTJWN","Joseph Magly","01M6C10AQ8A2RMED1Y1BSVY7NF",229,"MIT","https:\u002F\u002Fgithub.com\u002Fjmagly\u002Faiwg\u002Fblob\u002Fmain\u002Fagentic\u002Fcode\u002Fplugins\u002Fcodex-sdlc\u002Fskills\u002Fflow-performance-optimization\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"performance","flow performance optimization","Optimization","flow","performance optimization","text","sonnet","trigger","project","slo","bottleneck","sdlc","interactive","write","read","coding","testing","baseline","orchestrate","load testing","component","orchestration","directory","2026-11-10 10:12:30",false,"---\nnamespace: aiwg\nname: flow-performance-optimization\nplatforms: [all]\ndescription: Orchestrate continuous performance optimization with baseline establishment, bottleneck identification, optimization implementation, load testing, and SLO validation\ncommandHint:\n  argumentHint: '[trigger] [component] [project-directory] [--guidance \"text\"] [--interactive]'\n  allowedTools: 'Task, Read, Write, Glob, TodoWrite'\n  model: sonnet\n  category: sdlc-orchestration\n  orchestration: true\n  modelRole: coding\n  modelTier: standard\n---\n\n> **Declarative Flow (#1539):** This flow's orchestration is now also expressed as a\n> declarative YAML Flow at `flows\u002Fflow-performance-optimization.playbook.yaml` (source of truth for the step\n> sequence + gates). This SKILL.md remains the discoverable trigger surface and prose\n> reference. See epic #1534.\n\n# Performance Optimization Flow\n\n**You are the Performance Optimization Orchestrator** for systematic performance tuning, load testing, bottleneck analysis, and SLO validation.\n\n## Your Role\n\n**You orchestrate multi-agent workflows. You do NOT execute bash scripts.**\n\nWhen the user requests this flow (via natural language or explicit command):\n\n1. **Interpret the request** and confirm understanding\n2. **Read this template** as your orchestration guide\n3. **Extract agent assignments** and workflow steps\n4. **Delegate agents with the current provider-native orchestration mechanism** in the correct sequence\n5. **Synthesize results** and finalize artifacts\n6. **Report completion** with summary\n\n## Natural Language Triggers\n\nUsers may say:\n- \"Performance review\"\n- \"Optimize performance\"\n- \"Performance tuning\"\n- \"Improve performance\"\n- \"Fix slow response times\"\n- \"Application is too slow\"\n- \"Need better performance\"\n- \"SLO breach\"\n- \"Reduce latency\"\n- \"Improve throughput\"\n\nYou recognize these as requests for this performance optimization flow.\n\n## Parameter Handling\n\n### Optimization Triggers\n\n- **slo-breach**: Service Level Objective breached or at risk\n- **capacity-planning**: Anticipate scale requirements\n- **cost-reduction**: Reduce infrastructure costs\n- **user-complaint**: User-reported performance issues\n- **proactive**: Regular performance tuning cycle\n- **new-feature**: Performance testing for new functionality\n\n### --guidance Parameter\n\n**Purpose**: User provides upfront direction to tailor optimization priorities\n\n**Examples**:\n```\n--guidance \"Focus on database performance, seeing slow queries in production\"\n--guidance \"API latency is critical, p95 must be under 100ms\"\n--guidance \"Cost reduction priority, need to reduce infrastructure spend by 30%\"\n--guidance \"User complaints about page load times, frontend optimization needed\"\n```\n\n**How to Apply**:\n- Parse guidance for keywords: database, API, frontend, cost, latency, throughput\n- Adjust agent assignments (add database-optimizer for DB focus)\n- Modify optimization priorities (latency vs throughput vs cost)\n- Influence testing focus (load patterns, metrics to track)\n\n### --interactive Parameter\n\n**Purpose**: You ask 7 strategic questions to understand performance context\n\n**Questions to Ask** (if --interactive):\n\n```\nI'll ask 7 strategic questions to tailor the performance optimization to your needs:\n\nQ1: What performance issue are you addressing?\n    (e.g., slow response times, high costs, capacity limits)\n\nQ2: What's your current performance baseline?\n    (Help me understand starting point - p95 latency, throughput, error rate)\n\nQ3: What's your target performance improvement?\n    (Specific goals - reduce latency by 50%, double throughput, etc.)\n\nQ4: Where do you suspect bottlenecks?\n    (Database, API calls, frontend, infrastructure)\n\nQ5: What's your monitoring maturity?\n    (APM tools, metrics collection, observability stack)\n\nQ6: What's your acceptable optimization investment?\n    (Dev time budget, infrastructure cost changes allowed)\n\nQ7: What's your timeline pressure?\n    (Emergency fix needed vs. proactive optimization)\n\nBased on your answers, I'll adjust:\n- Agent assignments (specialized optimizers)\n- Optimization depth (quick wins vs. comprehensive)\n- Testing rigor (basic vs. extensive load testing)\n- Risk tolerance (safe vs. aggressive optimizations)\n```\n\n**Synthesize Guidance**: Combine answers into structured guidance for execution\n\n## Artifacts to Generate\n\n**Primary Deliverables**:\n- **Performance Baseline Report**: Current metrics → `.aiwg\u002Freports\u002Fperformance-baseline.md`\n- **Bottleneck Analysis**: Profiling results → `.aiwg\u002Freports\u002Fbottleneck-analysis.md`\n- **Optimization Plan**: Prioritized improvements → `.aiwg\u002Fplanning\u002Foptimization-plan.md`\n- **Load Test Results**: Performance validation → `.aiwg\u002Ftesting\u002Fload-test-results.md`\n- **SLO Compliance Report**: Target achievement → `.aiwg\u002Freports\u002Fslo-compliance.md`\n- **Optimization Summary**: ROI analysis → `.aiwg\u002Freports\u002Foptimization-summary.md`\n\n**Supporting Artifacts**:\n- Performance profiles (`.aiwg\u002Fworking\u002Fprofiles\u002F`)\n- POC implementations (`.aiwg\u002Fworking\u002Foptimizations\u002F`)\n- Test scripts (`.aiwg\u002Ftesting\u002Fscripts\u002F`)\n\n## Multi-Agent Orchestration Workflow\n\n### Step 1: Establish Performance Baseline\n\n**Purpose**: Define Service Level Indicators (SLIs) and establish current performance metrics\n\n**Your Actions**:\n\n1. **Check for Existing Performance Artifacts**:\n   ```\n   Read and verify presence of:\n   - .aiwg\u002Fdeployment\u002Fsli-card.md (if exists)\n   - .aiwg\u002Fdeployment\u002Fslo-card.md (if exists)\n   - .aiwg\u002Farchitecture\u002Fsoftware-architecture-doc.md (for performance targets)\n   ```\n\n2. **Launch Performance Analysis Agents** (parallel):\n   ```\n   # Agent 1: Reliability Engineer - Define SLIs\u002FSLOs\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Define SLIs and establish baseline\",\n       prompt=\"\"\"\n       Define Service Level Indicators (SLIs):\n       - Latency: p50, p95, p99 response times\n       - Throughput: Requests per second\n       - Error Rate: % of failed requests\n       - Availability: % uptime\n\n       Establish current baseline:\n       - Collect metrics for representative period (7-14 days if available)\n       - Identify peak and average load patterns\n       - Document current performance characteristics\n\n       Define Service Level Objectives (SLOs):\n       - Based on business requirements and user expectations\n       - Include error budget calculations\n       - Set realistic but ambitious targets\n\n       Use templates:\n       - $AIWG_ROOT\u002F...\u002Fdeployment\u002Fsli-card.md\n       - $AIWG_ROOT\u002F...\u002Fdeployment\u002Fslo-card.md\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fbaseline-metrics.md\n       \"\"\"\n   )\n\n   # Agent 2: Performance Engineer - Identify Critical Paths\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Identify performance-critical user journeys\",\n       prompt=\"\"\"\n       Analyze application to identify:\n\n       1. Critical User Journeys\n          - Most frequent operations\n          - Business-critical transactions\n          - User-facing bottlenecks\n\n       2. System Boundaries\n          - API endpoints and their usage patterns\n          - Database queries and access patterns\n          - External service dependencies\n\n       3. Current Monitoring\n          - Available metrics and logs\n          - APM tool coverage\n          - Gaps in observability\n\n       Document findings with specific paths and components.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fcritical-paths.md\n       \"\"\"\n   )\n   ```\n\n3. **Synthesize Baseline Report**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Create unified performance baseline report\",\n       prompt=\"\"\"\n       Read:\n       - .aiwg\u002Fworking\u002Fperformance\u002Fbaseline-metrics.md\n       - .aiwg\u002Fworking\u002Fperformance\u002Fcritical-paths.md\n\n       Create comprehensive baseline report:\n       1. Current Performance Metrics\n       2. SLI Definitions\n       3. SLO Targets\n       4. Critical User Journeys\n       5. Error Budget Status\n\n       Output: .aiwg\u002Freports\u002Fperformance-baseline.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Initialized performance baseline\n⏳ Establishing SLIs and current metrics...\n✓ Performance baseline complete: .aiwg\u002Freports\u002Fperformance-baseline.md\n  - p95 latency: {value}ms\n  - Throughput: {value} RPS\n  - Error rate: {value}%\n```\n\n### Step 2: Identify Performance Bottlenecks\n\n**Purpose**: Profile application and identify optimization opportunities\n\n**Your Actions**:\n\n1. **Launch Profiling and Analysis Agents** (parallel):\n   ```\n   # Agent 1: Performance Engineer - Application Profiling\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Profile application performance\",\n       prompt=\"\"\"\n       Conduct performance profiling:\n\n       1. CPU Profiling\n          - Identify hot paths and expensive operations\n          - Find inefficient algorithms (O(n²) operations)\n          - Detect excessive computation\n\n       2. Memory Profiling\n          - Memory allocation patterns\n          - Garbage collection pressure\n          - Memory leaks\n\n       3. I\u002FO Profiling\n          - Database query performance\n          - File system operations\n          - Network calls\n\n       4. Application Traces\n          - End-to-end request flow\n          - Service call latencies\n          - Async operation delays\n\n       Use template: $AIWG_ROOT\u002F...\u002Fanalysis-design\u002Fperformance-profile-card.md\n\n       Document top 5-10 bottlenecks with evidence.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fprofiling-results.md\n       \"\"\"\n   )\n\n   # Agent 2: Database Optimizer - Database Analysis\n   Task(\n       subagent_type=\"database-optimizer\",\n       description=\"Analyze database performance\",\n       prompt=\"\"\"\n       Analyze database performance issues:\n\n       1. Query Analysis\n          - Slow query log analysis\n          - Missing indexes identification\n          - N+1 query problems\n          - Inefficient joins\n\n       2. Schema Analysis\n          - Table structure optimization opportunities\n          - Denormalization candidates\n          - Partitioning opportunities\n\n       3. Connection Management\n          - Connection pool sizing\n          - Connection lifecycle\n          - Transaction boundaries\n\n       4. Caching Opportunities\n          - Query result caching\n          - Object caching\n          - Session caching\n\n       Provide specific optimization recommendations.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fdatabase-analysis.md\n       \"\"\"\n   )\n\n   # Agent 3: Software Implementer - Code Analysis\n   Task(\n       subagent_type=\"software-implementer\",\n       description=\"Analyze code-level optimization opportunities\",\n       prompt=\"\"\"\n       Review code for performance issues:\n\n       1. Algorithm Efficiency\n          - Time complexity issues\n          - Unnecessary loops\n          - Redundant computations\n\n       2. API Usage\n          - Synchronous calls that could be async\n          - Opportunities for batching\n          - Parallel execution opportunities\n\n       3. Resource Management\n          - Resource leaks\n          - Inefficient object creation\n          - String concatenation in loops\n\n       4. Frontend Performance (if applicable)\n          - Bundle size optimization\n          - Render performance\n          - Network request optimization\n\n       Document specific code locations and improvements.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fcode-analysis.md\n       \"\"\"\n   )\n   ```\n\n2. **Synthesize Bottleneck Analysis**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Create bottleneck analysis report\",\n       prompt=\"\"\"\n       Read all analysis results:\n       - .aiwg\u002Fworking\u002Fperformance\u002Fprofiling-results.md\n       - .aiwg\u002Fworking\u002Fperformance\u002Fdatabase-analysis.md\n       - .aiwg\u002Fworking\u002Fperformance\u002Fcode-analysis.md\n\n       Create prioritized bottleneck analysis:\n\n       For each bottleneck:\n       1. Description and root cause\n       2. Performance impact (% of total latency)\n       3. Affected user journeys\n       4. Optimization approach\n       5. Estimated improvement\n       6. Implementation effort\n\n       Prioritize by ROI (impact\u002Feffort).\n\n       Use template: $AIWG_ROOT\u002F...\u002Fintake\u002Foption-matrix-template.md for prioritization\n\n       Output: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Identifying performance bottlenecks...\n  ✓ Application profiling complete\n  ✓ Database analysis complete\n  ✓ Code analysis complete\n✓ Bottleneck analysis: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n  - Top bottleneck: {description} (impacts {%} of requests)\n```\n\n### Step 3: Plan and Prioritize Optimizations\n\n**Purpose**: Create actionable optimization plan with prioritized improvements\n\n**Your Actions**:\n\n1. **Calculate ROI and Create Plan**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Create optimization plan\",\n       prompt=\"\"\"\n       Read bottleneck analysis: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n\n       Create optimization plan:\n\n       1. Quick Wins (High impact, low effort)\n          - Implementation \u003C 1 day\n          - Measurable improvement\n          - Low risk\n\n       2. Strategic Improvements (High impact, medium effort)\n          - Implementation 2-5 days\n          - Significant improvement\n          - Moderate risk\n\n       3. Major Refactoring (High impact, high effort)\n          - Implementation > 5 days\n          - Transformative improvement\n          - Higher risk\n\n       For each optimization:\n       - Specific implementation steps\n       - Success criteria\n       - Testing approach\n       - Rollback plan\n\n       Output: .aiwg\u002Fplanning\u002Foptimization-plan.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Optimization plan created: .aiwg\u002Fplanning\u002Foptimization-plan.md\n  - Quick wins: {count} optimizations\n  - Strategic improvements: {count} optimizations\n  - Major refactoring: {count} optimizations\n```\n\n### Step 4: Implement Performance Optimizations\n\n**Purpose**: Execute prioritized optimizations with measurement\n\n**Your Actions**:\n\n1. **Launch Implementation Agents** (can be parallel for independent optimizations):\n   ```\n   # For each optimization in the plan:\n\n   # Database Optimizations\n   Task(\n       subagent_type=\"database-optimizer\",\n       description=\"Implement database optimizations\",\n       prompt=\"\"\"\n       Read optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n\n       Implement database optimizations:\n\n       1. Query Optimization\n          - Add missing indexes\n          - Rewrite inefficient queries\n          - Implement query result caching\n\n       2. Schema Optimization\n          - Denormalize where appropriate\n          - Add database-level constraints\n          - Implement partitioning if needed\n\n       3. Connection Optimization\n          - Tune connection pool settings\n          - Implement connection retry logic\n\n       Measure before\u002Fafter performance for each change.\n       Document implementation details and results.\n\n       Use template: $AIWG_ROOT\u002F...\u002Fimplementation\u002Fdesign-class-card.md\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Fdatabase-optimizations.md\n       \"\"\"\n   )\n\n   # Code Optimizations\n   Task(\n       subagent_type=\"software-implementer\",\n       description=\"Implement code optimizations\",\n       prompt=\"\"\"\n       Read optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n\n       Implement code optimizations:\n\n       1. Algorithm Improvements\n          - Replace inefficient algorithms\n          - Add memoization\u002Fcaching\n          - Implement lazy loading\n\n       2. Async Processing\n          - Convert sync to async operations\n          - Implement parallel processing\n          - Add background job processing\n\n       3. API Optimization\n          - Implement request batching\n          - Add response compression\n          - Optimize payload sizes\n\n       Include performance tests for each optimization.\n       Document implementation with before\u002Fafter metrics.\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Fcode-optimizations.md\n       \"\"\"\n   )\n\n   # Infrastructure Optimizations\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Implement infrastructure optimizations\",\n       prompt=\"\"\"\n       Read optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n\n       Implement infrastructure optimizations:\n\n       1. Caching Layer\n          - Configure Redis\u002FMemcached\n          - Implement cache warming\n          - Set appropriate TTLs\n\n       2. CDN Configuration\n          - Static asset caching\n          - Edge computing if applicable\n          - Compression settings\n\n       3. Load Balancing\n          - Algorithm tuning\n          - Connection draining\n          - Health check optimization\n\n       4. Auto-scaling\n          - Metric-based scaling rules\n          - Predictive scaling if available\n\n       Document configuration changes and impact.\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Finfrastructure-optimizations.md\n       \"\"\"\n   )\n   ```\n\n2. **Consolidate Implementation Results**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Consolidate optimization implementations\",\n       prompt=\"\"\"\n       Read all optimization results:\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fdatabase-optimizations.md\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fcode-optimizations.md\n       - .aiwg\u002Fworking\u002Foptimizations\u002Finfrastructure-optimizations.md\n\n       Create implementation summary:\n       1. Optimizations completed\n       2. Measured improvements (before\u002Fafter)\n       3. Failed attempts (what didn't work)\n       4. Pending optimizations\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Implementing optimizations...\n  ✓ Database optimizations: {X}% improvement\n  ✓ Code optimizations: {Y}% improvement\n  ✓ Infrastructure optimizations: {Z}% improvement\n✓ Optimizations implemented: .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n```\n\n### Step 5: Validate with Load Testing\n\n**Purpose**: Verify optimizations under realistic load conditions\n\n**Your Actions**:\n\n1. **Create Load Test Plan**:\n   ```\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Create load test plan\",\n       prompt=\"\"\"\n       Read baseline report: .aiwg\u002Freports\u002Fperformance-baseline.md\n       Read critical paths: .aiwg\u002Fworking\u002Fperformance\u002Fcritical-paths.md\n\n       Create load test plan covering:\n\n       1. Test Scenarios\n          - Baseline load test (normal traffic)\n          - Stress test (find breaking point)\n          - Spike test (sudden traffic increase)\n          - Soak test (sustained load over time)\n\n       2. Traffic Patterns\n          - User journey distribution\n          - Request rates\n          - Concurrent users\n          - Geographic distribution\n\n       3. Success Criteria\n          - SLO compliance\n          - No regressions\n          - Error rate threshold\n          - Resource utilization limits\n\n       Use template: $AIWG_ROOT\u002F...\u002Ftest\u002Fload-test-plan-template.md\n\n       Output: .aiwg\u002Ftesting\u002Fload-test-plan.md\n       \"\"\"\n   )\n   ```\n\n2. **Execute Load Tests**:\n   ```\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Execute load tests and analyze results\",\n       prompt=\"\"\"\n       Execute load tests per plan: .aiwg\u002Ftesting\u002Fload-test-plan.md\n\n       For each test scenario:\n\n       1. Baseline Load Test\n          - Measure p50, p95, p99 latencies\n          - Track throughput (RPS)\n          - Monitor error rates\n          - Resource utilization\n\n       2. Stress Test\n          - Identify breaking point\n          - Document failure modes\n          - Resource bottlenecks\n\n       3. Spike Test\n          - Auto-scaling response\n          - Recovery time\n          - Error handling\n\n       4. Soak Test\n          - Memory leak detection\n          - Performance degradation\n          - Resource exhaustion\n\n       Compare results to:\n       - Original baseline\n       - SLO targets\n       - Previous test runs\n\n       Use template: $AIWG_ROOT\u002F...\u002Ftest\u002Fperformance-test-card.md\n\n       Output: .aiwg\u002Ftesting\u002Fload-test-results.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Running load tests...\n  ✓ Baseline test complete: p95 = {X}ms (target: \u003C{Y}ms)\n  ✓ Stress test complete: Breaking point at {Z} RPS\n  ✓ Spike test complete: Recovery time = {T} seconds\n  ✓ Soak test complete: No degradation over 4 hours\n✓ Load test results: .aiwg\u002Ftesting\u002Fload-test-results.md\n```\n\n### Step 6: Validate SLO Compliance and Report\n\n**Purpose**: Confirm optimizations meet targets and document results\n\n**Your Actions**:\n\n1. **Validate SLO Compliance**:\n   ```\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Validate SLO compliance\",\n       prompt=\"\"\"\n       Read:\n       - .aiwg\u002Freports\u002Fperformance-baseline.md (original SLOs)\n       - .aiwg\u002Ftesting\u002Fload-test-results.md (test results)\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n\n       Validate SLO compliance:\n\n       1. Compare metrics to SLO targets\n          - Latency: p95, p99 vs targets\n          - Throughput: RPS vs target\n          - Error rate: % vs target\n          - Availability: Uptime vs target\n\n       2. Calculate error budget impact\n          - Budget consumed before optimization\n          - Budget consumed after optimization\n          - Budget saved\u002Frecovered\n\n       3. Identify any SLO breaches\n          - Which SLOs still not met\n          - Root cause\n          - Recommended next steps\n\n       Status: PASS | PARTIAL | FAIL\n\n       Output: .aiwg\u002Freports\u002Fslo-compliance.md\n       \"\"\"\n   )\n   ```\n\n2. **Generate Final Optimization Report**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Generate optimization summary report\",\n       prompt=\"\"\"\n       Read all optimization artifacts:\n       - .aiwg\u002Freports\u002Fperformance-baseline.md\n       - .aiwg\u002Freports\u002Fbottleneck-analysis.md\n       - .aiwg\u002Fplanning\u002Foptimization-plan.md\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n       - .aiwg\u002Ftesting\u002Fload-test-results.md\n       - .aiwg\u002Freports\u002Fslo-compliance.md\n\n       Generate comprehensive optimization report:\n\n       # Performance Optimization Report\n\n       ## Executive Summary\n       - Trigger: {optimization-trigger}\n       - Duration: {start} to {end}\n       - Overall improvement: {X}%\n       - SLO compliance: {PASS|PARTIAL|FAIL}\n\n       ## Performance Improvements\n\n       ### Before vs After Metrics\n       | Metric | Before | After | Improvement |\n       |--------|--------|-------|-------------|\n       | p50 Latency | Xms | Yms | Z% |\n       | p95 Latency | Xms | Yms | Z% |\n       | p99 Latency | Xms | Yms | Z% |\n       | Throughput | X RPS | Y RPS | Z% |\n       | Error Rate | X% | Y% | Z% |\n\n       ## Optimizations Implemented\n       {List each optimization with impact}\n\n       ## ROI Analysis\n       - Development effort: {hours\u002Fdays}\n       - Infrastructure cost change: ${amount}\u002Fmonth\n       - User experience impact: {metrics}\n       - Business impact: {revenue\u002Fconversion improvement}\n\n       ## Lessons Learned\n       - What worked well\n       - What didn't work\n       - Recommendations for future\n\n       ## Next Steps\n       - Additional optimization opportunities\n       - Monitoring improvements needed\n       - Follow-up schedule\n\n       Output: .aiwg\u002Freports\u002Foptimization-summary.md\n       \"\"\"\n   )\n   ```\n\n3. **Archive Working Files**:\n   ```\n   # You do this directly\n   Archive working files to: .aiwg\u002Farchive\u002F{date}\u002Fperformance-optimization\u002F\n   ```\n\n**Communicate Progress**:\n```\n⏳ Generating final reports...\n✓ SLO compliance validated: {PASS|PARTIAL|FAIL}\n✓ Optimization summary: .aiwg\u002Freports\u002Foptimization-summary.md\n  - Overall improvement: {X}%\n  - p95 latency: {before}ms → {after}ms\n  - Throughput: {before} → {after} RPS\n```\n\n## Quality Gates\n\nBefore marking workflow complete, verify:\n- [ ] Performance baseline established with SLOs\n- [ ] Bottlenecks identified and prioritized\n- [ ] Optimizations implemented with measurements\n- [ ] Load tests validate improvements\n- [ ] SLO compliance validated\n- [ ] ROI analysis completed\n\n## User Communication\n\n**At start**: Confirm understanding and list deliverables\n\n```\nUnderstood. I'll orchestrate the performance optimization flow.\n\nThis will analyze and optimize:\n- Performance bottlenecks\n- Database queries\n- Code efficiency\n- Infrastructure configuration\n\nDeliverables:\n- Performance baseline report\n- Bottleneck analysis\n- Optimization plan\n- Load test results\n- SLO compliance report\n- Optimization summary with ROI\n\nExpected duration: 20-30 minutes.\n\nStarting optimization workflow...\n```\n\n**During**: Update progress with metrics\n\n```\n✓ = Complete\n⏳ = In progress\n? = Improvement measured\n⚠️ = Issue found\n```\n\n**At end**: Summary with results\n\n```\n─────────────────────────────────────────────\nPerformance Optimization Complete\n─────────────────────────────────────────────\n\n**Overall Status**: SUCCESS\n**SLO Compliance**: PASS\n\n**Performance Improvements**:\n- p95 Latency: 450ms → 180ms (-60%)\n- Throughput: 500 → 1200 RPS (+140%)\n- Error Rate: 2.1% → 0.3% (-86%)\n\n**Key Optimizations**:\n✓ Database: Added 3 indexes, query optimization\n✓ Caching: Redis layer, 85% cache hit rate\n✓ Code: Async processing, algorithm improvements\n✓ Infrastructure: CDN, connection pooling\n\n**ROI Analysis**:\n- Development: 3 days\n- Cost Impact: -$800\u002Fmonth (reduced instances)\n- User Impact: Page loads 2.5x faster\n\n**Artifacts Generated**:\n- Performance baseline: .aiwg\u002Freports\u002Fperformance-baseline.md\n- Bottleneck analysis: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n- Optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n- Load test results: .aiwg\u002Ftesting\u002Fload-test-results.md\n- SLO compliance: .aiwg\u002Freports\u002Fslo-compliance.md\n- Final summary: .aiwg\u002Freports\u002Foptimization-summary.md\n\n**Next Steps**:\n- Monitor production metrics for 7 days\n- Schedule follow-up optimization cycle in 30 days\n- Consider implementing observability improvements\n─────────────────────────────────────────────\n```\n\n## Error Handling\n\n**If SLO Breach Critical**:\n```\n❌ Critical SLO breach detected\n\nMetric: {metric}\nCurrent: {value}\nTarget: {target}\nImpact: {user\u002Fbusiness impact}\n\nEmergency optimization required:\n1. Implement quick wins immediately\n2. Consider rollback if regression\n3. Escalate to stakeholders\n\nContinuing with emergency optimization protocol...\n```\n\n**If Optimization Failed**:\n```\n⚠️ Optimization did not improve performance\n\nOptimization: {description}\nExpected: {X}% improvement\nActual: {Y}% degradation\n\nActions:\n1. Rolling back change\n2. Re-analyzing bottleneck\n3. Trying alternative approach\n\nDocumenting in lessons learned...\n```\n\n**If Load Test Failure**:\n```\n❌ Load test failed\n\nTest: {scenario}\nFailure: {description}\nBreaking point: {metric}\n\nImpact:\n- Cannot handle expected load\n- SLO targets not achievable\n\nRecommendations:\n1. Infrastructure scaling required\n2. Additional optimizations needed\n3. Adjust SLO targets (with stakeholder approval)\n```\n\n## Success Criteria\n\nThis orchestration succeeds when:\n- [ ] Performance baseline established with clear SLOs\n- [ ] Top bottlenecks identified through profiling\n- [ ] Prioritized optimizations implemented\n- [ ] Load tests show measurable improvement\n- [ ] SLOs met or improvement plan defined\n- [ ] ROI analysis shows positive impact\n\n## Metrics to Track\n\n**During orchestration**:\n- Optimization velocity: optimizations\u002Fday\n- Performance improvement rate: % improvement\u002Foptimization\n- Test coverage: % of critical paths tested\n- SLO compliance rate: % of SLOs met\n- Error budget consumption: before vs after\n\n## References\n\n**Templates** (via $AIWG_ROOT):\n- SLI Card: `templates\u002Fdeployment\u002Fsli-card.md`\n- SLO Card: `templates\u002Fdeployment\u002Fslo-card.md`\n- Performance Profile: `templates\u002Fanalysis-design\u002Fperformance-profile-card.md`\n- Load Test Plan: `templates\u002Ftest\u002Fload-test-plan-template.md`\n- Performance Test Card: `templates\u002Ftest\u002Fperformance-test-card.md`\n- Option Matrix: `templates\u002Fintake\u002Foption-matrix-template.md`\n\n**Related Flows**:\n- `flow-monitoring-setup` - Establish observability\n- `flow-incident-response` - Handle performance incidents\n- `flow-capacity-planning` - Plan for scale\n\n**External References**:\n- Site Reliability Engineering (Google)\n- High Performance Browser Networking (Ilya Grigorik)","> **Declarative Flow (#1539):** This flow's orchestration is now also expressed as a\n> declarative YAML Flow at `flows\u002Fflow-performance-optimization.playbook.yaml` (source of truth for the step\n> sequence + gates). This SKILL.md remains the discoverable trigger surface and prose\n> reference. See epic #1534.\n\n# Performance Optimization Flow\n\n**You are the Performance Optimization Orchestrator** for systematic performance tuning, load testing, bottleneck analysis, and SLO validation.\n\n## Your Role\n\n**You orchestrate multi-agent workflows. You do NOT execute bash scripts.**\n\nWhen the user requests this flow (via natural language or explicit command):\n\n1. **Interpret the request** and confirm understanding\n2. **Read this template** as your orchestration guide\n3. **Extract agent assignments** and workflow steps\n4. **Delegate agents with the current provider-native orchestration mechanism** in the correct sequence\n5. **Synthesize results** and finalize artifacts\n6. **Report completion** with summary\n\n## Natural Language Triggers\n\nUsers may say:\n- \"Performance review\"\n- \"Optimize performance\"\n- \"Performance tuning\"\n- \"Improve performance\"\n- \"Fix slow response times\"\n- \"Application is too slow\"\n- \"Need better performance\"\n- \"SLO breach\"\n- \"Reduce latency\"\n- \"Improve throughput\"\n\nYou recognize these as requests for this performance optimization flow.\n\n## Parameter Handling\n\n### Optimization Triggers\n\n- **slo-breach**: Service Level Objective breached or at risk\n- **capacity-planning**: Anticipate scale requirements\n- **cost-reduction**: Reduce infrastructure costs\n- **user-complaint**: User-reported performance issues\n- **proactive**: Regular performance tuning cycle\n- **new-feature**: Performance testing for new functionality\n\n### --guidance Parameter\n\n**Purpose**: User provides upfront direction to tailor optimization priorities\n\n**Examples**:\n```\n--guidance \"Focus on database performance, seeing slow queries in production\"\n--guidance \"API latency is critical, p95 must be under 100ms\"\n--guidance \"Cost reduction priority, need to reduce infrastructure spend by 30%\"\n--guidance \"User complaints about page load times, frontend optimization needed\"\n```\n\n**How to Apply**:\n- Parse guidance for keywords: database, API, frontend, cost, latency, throughput\n- Adjust agent assignments (add database-optimizer for DB focus)\n- Modify optimization priorities (latency vs throughput vs cost)\n- Influence testing focus (load patterns, metrics to track)\n\n### --interactive Parameter\n\n**Purpose**: You ask 7 strategic questions to understand performance context\n\n**Questions to Ask** (if --interactive):\n\n```\nI'll ask 7 strategic questions to tailor the performance optimization to your needs:\n\nQ1: What performance issue are you addressing?\n    (e.g., slow response times, high costs, capacity limits)\n\nQ2: What's your current performance baseline?\n    (Help me understand starting point - p95 latency, throughput, error rate)\n\nQ3: What's your target performance improvement?\n    (Specific goals - reduce latency by 50%, double throughput, etc.)\n\nQ4: Where do you suspect bottlenecks?\n    (Database, API calls, frontend, infrastructure)\n\nQ5: What's your monitoring maturity?\n    (APM tools, metrics collection, observability stack)\n\nQ6: What's your acceptable optimization investment?\n    (Dev time budget, infrastructure cost changes allowed)\n\nQ7: What's your timeline pressure?\n    (Emergency fix needed vs. proactive optimization)\n\nBased on your answers, I'll adjust:\n- Agent assignments (specialized optimizers)\n- Optimization depth (quick wins vs. comprehensive)\n- Testing rigor (basic vs. extensive load testing)\n- Risk tolerance (safe vs. aggressive optimizations)\n```\n\n**Synthesize Guidance**: Combine answers into structured guidance for execution\n\n## Artifacts to Generate\n\n**Primary Deliverables**:\n- **Performance Baseline Report**: Current metrics → `.aiwg\u002Freports\u002Fperformance-baseline.md`\n- **Bottleneck Analysis**: Profiling results → `.aiwg\u002Freports\u002Fbottleneck-analysis.md`\n- **Optimization Plan**: Prioritized improvements → `.aiwg\u002Fplanning\u002Foptimization-plan.md`\n- **Load Test Results**: Performance validation → `.aiwg\u002Ftesting\u002Fload-test-results.md`\n- **SLO Compliance Report**: Target achievement → `.aiwg\u002Freports\u002Fslo-compliance.md`\n- **Optimization Summary**: ROI analysis → `.aiwg\u002Freports\u002Foptimization-summary.md`\n\n**Supporting Artifacts**:\n- Performance profiles (`.aiwg\u002Fworking\u002Fprofiles\u002F`)\n- POC implementations (`.aiwg\u002Fworking\u002Foptimizations\u002F`)\n- Test scripts (`.aiwg\u002Ftesting\u002Fscripts\u002F`)\n\n## Multi-Agent Orchestration Workflow\n\n### Step 1: Establish Performance Baseline\n\n**Purpose**: Define Service Level Indicators (SLIs) and establish current performance metrics\n\n**Your Actions**:\n\n1. **Check for Existing Performance Artifacts**:\n   ```\n   Read and verify presence of:\n   - .aiwg\u002Fdeployment\u002Fsli-card.md (if exists)\n   - .aiwg\u002Fdeployment\u002Fslo-card.md (if exists)\n   - .aiwg\u002Farchitecture\u002Fsoftware-architecture-doc.md (for performance targets)\n   ```\n\n2. **Launch Performance Analysis Agents** (parallel):\n   ```\n   # Agent 1: Reliability Engineer - Define SLIs\u002FSLOs\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Define SLIs and establish baseline\",\n       prompt=\"\"\"\n       Define Service Level Indicators (SLIs):\n       - Latency: p50, p95, p99 response times\n       - Throughput: Requests per second\n       - Error Rate: % of failed requests\n       - Availability: % uptime\n\n       Establish current baseline:\n       - Collect metrics for representative period (7-14 days if available)\n       - Identify peak and average load patterns\n       - Document current performance characteristics\n\n       Define Service Level Objectives (SLOs):\n       - Based on business requirements and user expectations\n       - Include error budget calculations\n       - Set realistic but ambitious targets\n\n       Use templates:\n       - $AIWG_ROOT\u002F...\u002Fdeployment\u002Fsli-card.md\n       - $AIWG_ROOT\u002F...\u002Fdeployment\u002Fslo-card.md\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fbaseline-metrics.md\n       \"\"\"\n   )\n\n   # Agent 2: Performance Engineer - Identify Critical Paths\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Identify performance-critical user journeys\",\n       prompt=\"\"\"\n       Analyze application to identify:\n\n       1. Critical User Journeys\n          - Most frequent operations\n          - Business-critical transactions\n          - User-facing bottlenecks\n\n       2. System Boundaries\n          - API endpoints and their usage patterns\n          - Database queries and access patterns\n          - External service dependencies\n\n       3. Current Monitoring\n          - Available metrics and logs\n          - APM tool coverage\n          - Gaps in observability\n\n       Document findings with specific paths and components.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fcritical-paths.md\n       \"\"\"\n   )\n   ```\n\n3. **Synthesize Baseline Report**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Create unified performance baseline report\",\n       prompt=\"\"\"\n       Read:\n       - .aiwg\u002Fworking\u002Fperformance\u002Fbaseline-metrics.md\n       - .aiwg\u002Fworking\u002Fperformance\u002Fcritical-paths.md\n\n       Create comprehensive baseline report:\n       1. Current Performance Metrics\n       2. SLI Definitions\n       3. SLO Targets\n       4. Critical User Journeys\n       5. Error Budget Status\n\n       Output: .aiwg\u002Freports\u002Fperformance-baseline.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Initialized performance baseline\n⏳ Establishing SLIs and current metrics...\n✓ Performance baseline complete: .aiwg\u002Freports\u002Fperformance-baseline.md\n  - p95 latency: {value}ms\n  - Throughput: {value} RPS\n  - Error rate: {value}%\n```\n\n### Step 2: Identify Performance Bottlenecks\n\n**Purpose**: Profile application and identify optimization opportunities\n\n**Your Actions**:\n\n1. **Launch Profiling and Analysis Agents** (parallel):\n   ```\n   # Agent 1: Performance Engineer - Application Profiling\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Profile application performance\",\n       prompt=\"\"\"\n       Conduct performance profiling:\n\n       1. CPU Profiling\n          - Identify hot paths and expensive operations\n          - Find inefficient algorithms (O(n²) operations)\n          - Detect excessive computation\n\n       2. Memory Profiling\n          - Memory allocation patterns\n          - Garbage collection pressure\n          - Memory leaks\n\n       3. I\u002FO Profiling\n          - Database query performance\n          - File system operations\n          - Network calls\n\n       4. Application Traces\n          - End-to-end request flow\n          - Service call latencies\n          - Async operation delays\n\n       Use template: $AIWG_ROOT\u002F...\u002Fanalysis-design\u002Fperformance-profile-card.md\n\n       Document top 5-10 bottlenecks with evidence.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fprofiling-results.md\n       \"\"\"\n   )\n\n   # Agent 2: Database Optimizer - Database Analysis\n   Task(\n       subagent_type=\"database-optimizer\",\n       description=\"Analyze database performance\",\n       prompt=\"\"\"\n       Analyze database performance issues:\n\n       1. Query Analysis\n          - Slow query log analysis\n          - Missing indexes identification\n          - N+1 query problems\n          - Inefficient joins\n\n       2. Schema Analysis\n          - Table structure optimization opportunities\n          - Denormalization candidates\n          - Partitioning opportunities\n\n       3. Connection Management\n          - Connection pool sizing\n          - Connection lifecycle\n          - Transaction boundaries\n\n       4. Caching Opportunities\n          - Query result caching\n          - Object caching\n          - Session caching\n\n       Provide specific optimization recommendations.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fdatabase-analysis.md\n       \"\"\"\n   )\n\n   # Agent 3: Software Implementer - Code Analysis\n   Task(\n       subagent_type=\"software-implementer\",\n       description=\"Analyze code-level optimization opportunities\",\n       prompt=\"\"\"\n       Review code for performance issues:\n\n       1. Algorithm Efficiency\n          - Time complexity issues\n          - Unnecessary loops\n          - Redundant computations\n\n       2. API Usage\n          - Synchronous calls that could be async\n          - Opportunities for batching\n          - Parallel execution opportunities\n\n       3. Resource Management\n          - Resource leaks\n          - Inefficient object creation\n          - String concatenation in loops\n\n       4. Frontend Performance (if applicable)\n          - Bundle size optimization\n          - Render performance\n          - Network request optimization\n\n       Document specific code locations and improvements.\n\n       Output: .aiwg\u002Fworking\u002Fperformance\u002Fcode-analysis.md\n       \"\"\"\n   )\n   ```\n\n2. **Synthesize Bottleneck Analysis**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Create bottleneck analysis report\",\n       prompt=\"\"\"\n       Read all analysis results:\n       - .aiwg\u002Fworking\u002Fperformance\u002Fprofiling-results.md\n       - .aiwg\u002Fworking\u002Fperformance\u002Fdatabase-analysis.md\n       - .aiwg\u002Fworking\u002Fperformance\u002Fcode-analysis.md\n\n       Create prioritized bottleneck analysis:\n\n       For each bottleneck:\n       1. Description and root cause\n       2. Performance impact (% of total latency)\n       3. Affected user journeys\n       4. Optimization approach\n       5. Estimated improvement\n       6. Implementation effort\n\n       Prioritize by ROI (impact\u002Feffort).\n\n       Use template: $AIWG_ROOT\u002F...\u002Fintake\u002Foption-matrix-template.md for prioritization\n\n       Output: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Identifying performance bottlenecks...\n  ✓ Application profiling complete\n  ✓ Database analysis complete\n  ✓ Code analysis complete\n✓ Bottleneck analysis: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n  - Top bottleneck: {description} (impacts {%} of requests)\n```\n\n### Step 3: Plan and Prioritize Optimizations\n\n**Purpose**: Create actionable optimization plan with prioritized improvements\n\n**Your Actions**:\n\n1. **Calculate ROI and Create Plan**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Create optimization plan\",\n       prompt=\"\"\"\n       Read bottleneck analysis: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n\n       Create optimization plan:\n\n       1. Quick Wins (High impact, low effort)\n          - Implementation \u003C 1 day\n          - Measurable improvement\n          - Low risk\n\n       2. Strategic Improvements (High impact, medium effort)\n          - Implementation 2-5 days\n          - Significant improvement\n          - Moderate risk\n\n       3. Major Refactoring (High impact, high effort)\n          - Implementation > 5 days\n          - Transformative improvement\n          - Higher risk\n\n       For each optimization:\n       - Specific implementation steps\n       - Success criteria\n       - Testing approach\n       - Rollback plan\n\n       Output: .aiwg\u002Fplanning\u002Foptimization-plan.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Optimization plan created: .aiwg\u002Fplanning\u002Foptimization-plan.md\n  - Quick wins: {count} optimizations\n  - Strategic improvements: {count} optimizations\n  - Major refactoring: {count} optimizations\n```\n\n### Step 4: Implement Performance Optimizations\n\n**Purpose**: Execute prioritized optimizations with measurement\n\n**Your Actions**:\n\n1. **Launch Implementation Agents** (can be parallel for independent optimizations):\n   ```\n   # For each optimization in the plan:\n\n   # Database Optimizations\n   Task(\n       subagent_type=\"database-optimizer\",\n       description=\"Implement database optimizations\",\n       prompt=\"\"\"\n       Read optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n\n       Implement database optimizations:\n\n       1. Query Optimization\n          - Add missing indexes\n          - Rewrite inefficient queries\n          - Implement query result caching\n\n       2. Schema Optimization\n          - Denormalize where appropriate\n          - Add database-level constraints\n          - Implement partitioning if needed\n\n       3. Connection Optimization\n          - Tune connection pool settings\n          - Implement connection retry logic\n\n       Measure before\u002Fafter performance for each change.\n       Document implementation details and results.\n\n       Use template: $AIWG_ROOT\u002F...\u002Fimplementation\u002Fdesign-class-card.md\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Fdatabase-optimizations.md\n       \"\"\"\n   )\n\n   # Code Optimizations\n   Task(\n       subagent_type=\"software-implementer\",\n       description=\"Implement code optimizations\",\n       prompt=\"\"\"\n       Read optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n\n       Implement code optimizations:\n\n       1. Algorithm Improvements\n          - Replace inefficient algorithms\n          - Add memoization\u002Fcaching\n          - Implement lazy loading\n\n       2. Async Processing\n          - Convert sync to async operations\n          - Implement parallel processing\n          - Add background job processing\n\n       3. API Optimization\n          - Implement request batching\n          - Add response compression\n          - Optimize payload sizes\n\n       Include performance tests for each optimization.\n       Document implementation with before\u002Fafter metrics.\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Fcode-optimizations.md\n       \"\"\"\n   )\n\n   # Infrastructure Optimizations\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Implement infrastructure optimizations\",\n       prompt=\"\"\"\n       Read optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n\n       Implement infrastructure optimizations:\n\n       1. Caching Layer\n          - Configure Redis\u002FMemcached\n          - Implement cache warming\n          - Set appropriate TTLs\n\n       2. CDN Configuration\n          - Static asset caching\n          - Edge computing if applicable\n          - Compression settings\n\n       3. Load Balancing\n          - Algorithm tuning\n          - Connection draining\n          - Health check optimization\n\n       4. Auto-scaling\n          - Metric-based scaling rules\n          - Predictive scaling if available\n\n       Document configuration changes and impact.\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Finfrastructure-optimizations.md\n       \"\"\"\n   )\n   ```\n\n2. **Consolidate Implementation Results**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Consolidate optimization implementations\",\n       prompt=\"\"\"\n       Read all optimization results:\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fdatabase-optimizations.md\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fcode-optimizations.md\n       - .aiwg\u002Fworking\u002Foptimizations\u002Finfrastructure-optimizations.md\n\n       Create implementation summary:\n       1. Optimizations completed\n       2. Measured improvements (before\u002Fafter)\n       3. Failed attempts (what didn't work)\n       4. Pending optimizations\n\n       Output: .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Implementing optimizations...\n  ✓ Database optimizations: {X}% improvement\n  ✓ Code optimizations: {Y}% improvement\n  ✓ Infrastructure optimizations: {Z}% improvement\n✓ Optimizations implemented: .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n```\n\n### Step 5: Validate with Load Testing\n\n**Purpose**: Verify optimizations under realistic load conditions\n\n**Your Actions**:\n\n1. **Create Load Test Plan**:\n   ```\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Create load test plan\",\n       prompt=\"\"\"\n       Read baseline report: .aiwg\u002Freports\u002Fperformance-baseline.md\n       Read critical paths: .aiwg\u002Fworking\u002Fperformance\u002Fcritical-paths.md\n\n       Create load test plan covering:\n\n       1. Test Scenarios\n          - Baseline load test (normal traffic)\n          - Stress test (find breaking point)\n          - Spike test (sudden traffic increase)\n          - Soak test (sustained load over time)\n\n       2. Traffic Patterns\n          - User journey distribution\n          - Request rates\n          - Concurrent users\n          - Geographic distribution\n\n       3. Success Criteria\n          - SLO compliance\n          - No regressions\n          - Error rate threshold\n          - Resource utilization limits\n\n       Use template: $AIWG_ROOT\u002F...\u002Ftest\u002Fload-test-plan-template.md\n\n       Output: .aiwg\u002Ftesting\u002Fload-test-plan.md\n       \"\"\"\n   )\n   ```\n\n2. **Execute Load Tests**:\n   ```\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Execute load tests and analyze results\",\n       prompt=\"\"\"\n       Execute load tests per plan: .aiwg\u002Ftesting\u002Fload-test-plan.md\n\n       For each test scenario:\n\n       1. Baseline Load Test\n          - Measure p50, p95, p99 latencies\n          - Track throughput (RPS)\n          - Monitor error rates\n          - Resource utilization\n\n       2. Stress Test\n          - Identify breaking point\n          - Document failure modes\n          - Resource bottlenecks\n\n       3. Spike Test\n          - Auto-scaling response\n          - Recovery time\n          - Error handling\n\n       4. Soak Test\n          - Memory leak detection\n          - Performance degradation\n          - Resource exhaustion\n\n       Compare results to:\n       - Original baseline\n       - SLO targets\n       - Previous test runs\n\n       Use template: $AIWG_ROOT\u002F...\u002Ftest\u002Fperformance-test-card.md\n\n       Output: .aiwg\u002Ftesting\u002Fload-test-results.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Running load tests...\n  ✓ Baseline test complete: p95 = {X}ms (target: \u003C{Y}ms)\n  ✓ Stress test complete: Breaking point at {Z} RPS\n  ✓ Spike test complete: Recovery time = {T} seconds\n  ✓ Soak test complete: No degradation over 4 hours\n✓ Load test results: .aiwg\u002Ftesting\u002Fload-test-results.md\n```\n\n### Step 6: Validate SLO Compliance and Report\n\n**Purpose**: Confirm optimizations meet targets and document results\n\n**Your Actions**:\n\n1. **Validate SLO Compliance**:\n   ```\n   Task(\n       subagent_type=\"reliability-engineer\",\n       description=\"Validate SLO compliance\",\n       prompt=\"\"\"\n       Read:\n       - .aiwg\u002Freports\u002Fperformance-baseline.md (original SLOs)\n       - .aiwg\u002Ftesting\u002Fload-test-results.md (test results)\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n\n       Validate SLO compliance:\n\n       1. Compare metrics to SLO targets\n          - Latency: p95, p99 vs targets\n          - Throughput: RPS vs target\n          - Error rate: % vs target\n          - Availability: Uptime vs target\n\n       2. Calculate error budget impact\n          - Budget consumed before optimization\n          - Budget consumed after optimization\n          - Budget saved\u002Frecovered\n\n       3. Identify any SLO breaches\n          - Which SLOs still not met\n          - Root cause\n          - Recommended next steps\n\n       Status: PASS | PARTIAL | FAIL\n\n       Output: .aiwg\u002Freports\u002Fslo-compliance.md\n       \"\"\"\n   )\n   ```\n\n2. **Generate Final Optimization Report**:\n   ```\n   Task(\n       subagent_type=\"performance-engineer\",\n       description=\"Generate optimization summary report\",\n       prompt=\"\"\"\n       Read all optimization artifacts:\n       - .aiwg\u002Freports\u002Fperformance-baseline.md\n       - .aiwg\u002Freports\u002Fbottleneck-analysis.md\n       - .aiwg\u002Fplanning\u002Foptimization-plan.md\n       - .aiwg\u002Fworking\u002Foptimizations\u002Fimplementation-summary.md\n       - .aiwg\u002Ftesting\u002Fload-test-results.md\n       - .aiwg\u002Freports\u002Fslo-compliance.md\n\n       Generate comprehensive optimization report:\n\n       # Performance Optimization Report\n\n       ## Executive Summary\n       - Trigger: {optimization-trigger}\n       - Duration: {start} to {end}\n       - Overall improvement: {X}%\n       - SLO compliance: {PASS|PARTIAL|FAIL}\n\n       ## Performance Improvements\n\n       ### Before vs After Metrics\n       | Metric | Before | After | Improvement |\n       |--------|--------|-------|-------------|\n       | p50 Latency | Xms | Yms | Z% |\n       | p95 Latency | Xms | Yms | Z% |\n       | p99 Latency | Xms | Yms | Z% |\n       | Throughput | X RPS | Y RPS | Z% |\n       | Error Rate | X% | Y% | Z% |\n\n       ## Optimizations Implemented\n       {List each optimization with impact}\n\n       ## ROI Analysis\n       - Development effort: {hours\u002Fdays}\n       - Infrastructure cost change: ${amount}\u002Fmonth\n       - User experience impact: {metrics}\n       - Business impact: {revenue\u002Fconversion improvement}\n\n       ## Lessons Learned\n       - What worked well\n       - What didn't work\n       - Recommendations for future\n\n       ## Next Steps\n       - Additional optimization opportunities\n       - Monitoring improvements needed\n       - Follow-up schedule\n\n       Output: .aiwg\u002Freports\u002Foptimization-summary.md\n       \"\"\"\n   )\n   ```\n\n3. **Archive Working Files**:\n   ```\n   # You do this directly\n   Archive working files to: .aiwg\u002Farchive\u002F{date}\u002Fperformance-optimization\u002F\n   ```\n\n**Communicate Progress**:\n```\n⏳ Generating final reports...\n✓ SLO compliance validated: {PASS|PARTIAL|FAIL}\n✓ Optimization summary: .aiwg\u002Freports\u002Foptimization-summary.md\n  - Overall improvement: {X}%\n  - p95 latency: {before}ms → {after}ms\n  - Throughput: {before} → {after} RPS\n```\n\n## Quality Gates\n\nBefore marking workflow complete, verify:\n- [ ] Performance baseline established with SLOs\n- [ ] Bottlenecks identified and prioritized\n- [ ] Optimizations implemented with measurements\n- [ ] Load tests validate improvements\n- [ ] SLO compliance validated\n- [ ] ROI analysis completed\n\n## User Communication\n\n**At start**: Confirm understanding and list deliverables\n\n```\nUnderstood. I'll orchestrate the performance optimization flow.\n\nThis will analyze and optimize:\n- Performance bottlenecks\n- Database queries\n- Code efficiency\n- Infrastructure configuration\n\nDeliverables:\n- Performance baseline report\n- Bottleneck analysis\n- Optimization plan\n- Load test results\n- SLO compliance report\n- Optimization summary with ROI\n\nExpected duration: 20-30 minutes.\n\nStarting optimization workflow...\n```\n\n**During**: Update progress with metrics\n\n```\n✓ = Complete\n⏳ = In progress\n? = Improvement measured\n⚠️ = Issue found\n```\n\n**At end**: Summary with results\n\n```\n─────────────────────────────────────────────\nPerformance Optimization Complete\n─────────────────────────────────────────────\n\n**Overall Status**: SUCCESS\n**SLO Compliance**: PASS\n\n**Performance Improvements**:\n- p95 Latency: 450ms → 180ms (-60%)\n- Throughput: 500 → 1200 RPS (+140%)\n- Error Rate: 2.1% → 0.3% (-86%)\n\n**Key Optimizations**:\n✓ Database: Added 3 indexes, query optimization\n✓ Caching: Redis layer, 85% cache hit rate\n✓ Code: Async processing, algorithm improvements\n✓ Infrastructure: CDN, connection pooling\n\n**ROI Analysis**:\n- Development: 3 days\n- Cost Impact: -$800\u002Fmonth (reduced instances)\n- User Impact: Page loads 2.5x faster\n\n**Artifacts Generated**:\n- Performance baseline: .aiwg\u002Freports\u002Fperformance-baseline.md\n- Bottleneck analysis: .aiwg\u002Freports\u002Fbottleneck-analysis.md\n- Optimization plan: .aiwg\u002Fplanning\u002Foptimization-plan.md\n- Load test results: .aiwg\u002Ftesting\u002Fload-test-results.md\n- SLO compliance: .aiwg\u002Freports\u002Fslo-compliance.md\n- Final summary: .aiwg\u002Freports\u002Foptimization-summary.md\n\n**Next Steps**:\n- Monitor production metrics for 7 days\n- Schedule follow-up optimization cycle in 30 days\n- Consider implementing observability improvements\n─────────────────────────────────────────────\n```\n\n## Error Handling\n\n**If SLO Breach Critical**:\n```\n❌ Critical SLO breach detected\n\nMetric: {metric}\nCurrent: {value}\nTarget: {target}\nImpact: {user\u002Fbusiness impact}\n\nEmergency optimization required:\n1. Implement quick wins immediately\n2. Consider rollback if regression\n3. Escalate to stakeholders\n\nContinuing with emergency optimization protocol...\n```\n\n**If Optimization Failed**:\n```\n⚠️ Optimization did not improve performance\n\nOptimization: {description}\nExpected: {X}% improvement\nActual: {Y}% degradation\n\nActions:\n1. Rolling back change\n2. Re-analyzing bottleneck\n3. Trying alternative approach\n\nDocumenting in lessons learned...\n```\n\n**If Load Test Failure**:\n```\n❌ Load test failed\n\nTest: {scenario}\nFailure: {description}\nBreaking point: {metric}\n\nImpact:\n- Cannot handle expected load\n- SLO targets not achievable\n\nRecommendations:\n1. Infrastructure scaling required\n2. Additional optimizations needed\n3. Adjust SLO targets (with stakeholder approval)\n```\n\n## Success Criteria\n\nThis orchestration succeeds when:\n- [ ] Performance baseline established with clear SLOs\n- [ ] Top bottlenecks identified through profiling\n- [ ] Prioritized optimizations implemented\n- [ ] Load tests show measurable improvement\n- [ ] SLOs met or improvement plan defined\n- [ ] ROI analysis shows positive impact\n\n## Metrics to Track\n\n**During orchestration**:\n- Optimization velocity: optimizations\u002Fday\n- Performance improvement rate: % improvement\u002Foptimization\n- Test coverage: % of critical paths tested\n- SLO compliance rate: % of SLOs met\n- Error budget consumption: before vs after\n\n## References\n\n**Templates** (via $AIWG_ROOT):\n- SLI Card: `templates\u002Fdeployment\u002Fsli-card.md`\n- SLO Card: `templates\u002Fdeployment\u002Fslo-card.md`\n- Performance Profile: `templates\u002Fanalysis-design\u002Fperformance-profile-card.md`\n- Load Test Plan: 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