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Comprehensive benchmarking and optimization suite.","manykarim\u002Frobotframework-agentguard","manykarim","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F61293164?v=4","01M71S15ZAXR15JHKM1N7BHT21","Many Kasiriha","01M71S160CWQXETSCYWXDVNYJ2",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmanykarim\u002Frobotframework-agentguard\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fv3-performance-optimization\u002FSKILL.md",[],[19,20,21,5,22,23,24,25,26,27,28,29,30,31],"performance","Optimization","performance optimization","v3","memory","benchmarking","search","flash","attention","targets","Flash Attention","suite","achieve","2026-11-10 13:02:02",false,"---\nname: \"V3 Performance Optimization\"\ndescription: \"Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.\"\n---\n\n# V3 Performance Optimization\n\n## What This Skill Does\n\nValidates and optimizes claude-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.\n\n## Quick Start\n\n```bash\n# Initialize performance optimization\nTask(\"Performance baseline\", \"Establish v2 performance benchmarks\", \"v3-performance-engineer\")\n\n# Target validation (parallel)\nTask(\"Flash Attention\", \"Validate 2.49x-7.47x speedup target\", \"v3-performance-engineer\")\nTask(\"Search optimization\", \"Validate 150x-12,500x search improvement\", \"v3-performance-engineer\")\nTask(\"Memory optimization\", \"Achieve 50-75% memory reduction\", \"v3-performance-engineer\")\n```\n\n## Performance Target Matrix\n\n### Flash Attention Revolution\n```\n┌─────────────────────────────────────────┐\n│           FLASH ATTENTION               │\n├─────────────────────────────────────────┤\n│  Baseline: Standard attention           │\n│  Target:   2.49x - 7.47x speedup       │\n│  Memory:   50-75% reduction             │\n│  Latency:  Sub-millisecond processing   │\n└─────────────────────────────────────────┘\n```\n\n### Search Performance Revolution\n```\n┌─────────────────────────────────────────┐\n│            SEARCH OPTIMIZATION         │\n├─────────────────────────────────────────┤\n│  Current:  O(n) linear search           │\n│  Target:   150x - 12,500x improvement   │\n│  Method:   HNSW indexing                │\n│  Latency:  \u003C100ms for 1M+ entries       │\n└─────────────────────────────────────────┘\n```\n\n## Comprehensive Benchmark Suite\n\n### Startup Performance\n```typescript\nclass StartupBenchmarks {\n  async benchmarkColdStart(): Promise\u003CBenchmarkResult> {\n    const startTime = performance.now();\n\n    await this.initializeCLI();\n    await this.initializeMCPServer();\n    await this.spawnTestAgent();\n\n    const totalTime = performance.now() - startTime;\n\n    return {\n      total: totalTime,\n      target: 500, \u002F\u002F ms\n      achieved: totalTime \u003C 500\n    };\n  }\n}\n```\n\n### Memory Operation Benchmarks\n```typescript\nclass MemoryBenchmarks {\n  async benchmarkVectorSearch(): Promise\u003CSearchBenchmark> {\n    const queries = this.generateTestQueries(10000);\n\n    \u002F\u002F Baseline: Current linear search\n    const baselineTime = await this.timeOperation(() =>\n      this.currentMemory.searchAll(queries)\n    );\n\n    \u002F\u002F Target: HNSW search\n    const hnswTime = await this.timeOperation(() =>\n      this.agentDBMemory.hnswSearchAll(queries)\n    );\n\n    const improvement = baselineTime \u002F hnswTime;\n\n    return {\n      baseline: baselineTime,\n      hnsw: hnswTime,\n      improvement,\n      targetRange: [150, 12500],\n      achieved: improvement >= 150\n    };\n  }\n\n  async benchmarkMemoryUsage(): Promise\u003CMemoryBenchmark> {\n    const baseline = process.memoryUsage().heapUsed;\n\n    await this.loadTestDataset();\n    const withData = process.memoryUsage().heapUsed;\n\n    await this.enableOptimization();\n    const optimized = process.memoryUsage().heapUsed;\n\n    const reduction = (withData - optimized) \u002F withData;\n\n    return {\n      baseline,\n      withData,\n      optimized,\n      reductionPercent: reduction * 100,\n      targetReduction: [50, 75],\n      achieved: reduction >= 0.5\n    };\n  }\n}\n```\n\n### Swarm Coordination Benchmarks\n```typescript\nclass SwarmBenchmarks {\n  async benchmark15AgentCoordination(): Promise\u003CSwarmBenchmark> {\n    const agents = await this.spawn15Agents();\n\n    \u002F\u002F Coordination latency\n    const coordinationTime = await this.timeOperation(() =>\n      this.coordinateSwarmTask(agents)\n    );\n\n    \u002F\u002F Task decomposition\n    const decompositionTime = await this.timeOperation(() =>\n      this.decomposeComplexTask()\n    );\n\n    \u002F\u002F Consensus achievement\n    const consensusTime = await this.timeOperation(() =>\n      this.achieveSwarmConsensus(agents)\n    );\n\n    return {\n      coordination: coordinationTime,\n      decomposition: decompositionTime,\n      consensus: consensusTime,\n      agentCount: 15,\n      efficiency: this.calculateEfficiency(agents)\n    };\n  }\n}\n```\n\n### Flash Attention Benchmarks\n```typescript\nclass AttentionBenchmarks {\n  async benchmarkFlashAttention(): Promise\u003CAttentionBenchmark> {\n    const sequences = this.generateSequences([512, 1024, 2048, 4096]);\n    const results = [];\n\n    for (const sequence of sequences) {\n      \u002F\u002F Baseline attention\n      const baselineResult = await this.benchmarkStandardAttention(sequence);\n\n      \u002F\u002F Flash attention\n      const flashResult = await this.benchmarkFlashAttention(sequence);\n\n      results.push({\n        sequenceLength: sequence.length,\n        speedup: baselineResult.time \u002F flashResult.time,\n        memoryReduction: (baselineResult.memory - flashResult.memory) \u002F baselineResult.memory,\n        targetSpeedup: [2.49, 7.47],\n        achieved: this.checkTarget(flashResult, [2.49, 7.47])\n      });\n    }\n\n    return {\n      results,\n      averageSpeedup: this.calculateAverage(results, 'speedup'),\n      averageMemoryReduction: this.calculateAverage(results, 'memoryReduction')\n    };\n  }\n}\n```\n\n### SONA Learning Benchmarks\n```typescript\nclass SONABenchmarks {\n  async benchmarkAdaptationTime(): Promise\u003CSONABenchmark> {\n    const scenarios = [\n      'pattern_recognition',\n      'task_optimization',\n      'error_correction',\n      'performance_tuning'\n    ];\n\n    const results = [];\n\n    for (const scenario of scenarios) {\n      const startTime = performance.hrtime.bigint();\n      await this.sona.adapt(scenario);\n      const endTime = performance.hrtime.bigint();\n\n      const adaptationTimeMs = Number(endTime - startTime) \u002F 1000000;\n\n      results.push({\n        scenario,\n        adaptationTime: adaptationTimeMs,\n        target: 0.05, \u002F\u002F ms\n        achieved: adaptationTimeMs \u003C= 0.05\n      });\n    }\n\n    return {\n      scenarios: results,\n      averageTime: results.reduce((sum, r) => sum + r.adaptationTime, 0) \u002F results.length,\n      successRate: results.filter(r => r.achieved).length \u002F results.length\n    };\n  }\n}\n```\n\n## Performance Monitoring Dashboard\n\n### Real-time Metrics\n```typescript\nclass PerformanceMonitor {\n  async collectMetrics(): Promise\u003CPerformanceSnapshot> {\n    return {\n      timestamp: Date.now(),\n      flashAttention: await this.measureFlashAttention(),\n      searchPerformance: await this.measureSearchSpeed(),\n      memoryUsage: await this.measureMemoryEfficiency(),\n      startupTime: await this.measureStartupLatency(),\n      sonaAdaptation: await this.measureSONASpeed(),\n      swarmCoordination: await this.measureSwarmEfficiency()\n    };\n  }\n\n  async generateReport(): Promise\u003CPerformanceReport> {\n    const snapshot = await this.collectMetrics();\n\n    return {\n      summary: this.generateSummary(snapshot),\n      achievements: this.checkTargetAchievements(snapshot),\n      trends: this.analyzeTrends(),\n      recommendations: this.generateOptimizations(),\n      regressions: await this.detectRegressions()\n    };\n  }\n}\n```\n\n### Continuous Regression Detection\n```typescript\nclass PerformanceRegression {\n  async detectRegressions(): Promise\u003CRegressionReport> {\n    const current = await this.runFullBenchmark();\n    const baseline = await this.getBaseline();\n\n    const regressions = [];\n\n    for (const [metric, currentValue] of Object.entries(current)) {\n      const baselineValue = baseline[metric];\n      const change = (currentValue - baselineValue) \u002F baselineValue;\n\n      if (change \u003C -0.05) { \u002F\u002F 5% regression threshold\n        regressions.push({\n          metric,\n          baseline: baselineValue,\n          current: currentValue,\n          regressionPercent: change * 100,\n          severity: this.classifyRegression(change)\n        });\n      }\n    }\n\n    return {\n      hasRegressions: regressions.length > 0,\n      regressions,\n      recommendations: this.generateRegressionFixes(regressions)\n    };\n  }\n}\n```\n\n## Optimization Strategies\n\n### Memory Optimization\n```typescript\nclass MemoryOptimization {\n  async optimizeMemoryUsage(): Promise\u003COptimizationResult> {\n    \u002F\u002F Implement memory pooling\n    await this.setupMemoryPools();\n\n    \u002F\u002F Enable garbage collection tuning\n    await this.optimizeGarbageCollection();\n\n    \u002F\u002F Implement object reuse patterns\n    await this.setupObjectPools();\n\n    \u002F\u002F Enable memory compression\n    await this.enableMemoryCompression();\n\n    return this.validateMemoryReduction();\n  }\n}\n```\n\n### CPU Optimization\n```typescript\nclass CPUOptimization {\n  async optimizeCPUUsage(): Promise\u003COptimizationResult> {\n    \u002F\u002F Implement worker thread pools\n    await this.setupWorkerThreads();\n\n    \u002F\u002F Enable CPU-specific optimizations\n    await this.enableSIMDInstructions();\n\n    \u002F\u002F Implement task batching\n    await this.optimizeTaskBatching();\n\n    return this.validateCPUImprovement();\n  }\n}\n```\n\n## Target Validation Framework\n\n### Performance Gates\n```typescript\nclass PerformanceGates {\n  async validateAllTargets(): Promise\u003CValidationReport> {\n    const results = await Promise.all([\n      this.validateFlashAttention(),     \u002F\u002F 2.49x-7.47x\n      this.validateSearchPerformance(),  \u002F\u002F 150x-12,500x\n      this.validateMemoryReduction(),    \u002F\u002F 50-75%\n      this.validateStartupTime(),        \u002F\u002F \u003C500ms\n      this.validateSONAAdaptation()      \u002F\u002F \u003C0.05ms\n    ]);\n\n    return {\n      allTargetsAchieved: results.every(r => r.achieved),\n      results,\n      overallScore: this.calculateOverallScore(results),\n      recommendations: this.generateRecommendations(results)\n    };\n  }\n}\n```\n\n## Success Metrics\n\n### Primary Targets\n- [ ] **Flash Attention**: 2.49x-7.47x speedup validated\n- [ ] **Search Performance**: 150x-12,500x improvement confirmed\n- [ ] **Memory Reduction**: 50-75% usage optimization achieved\n- [ ] **Startup Time**: \u003C500ms cold start consistently\n- [ ] **SONA Adaptation**: \u003C0.05ms learning response time\n- [ ] **15-Agent Coordination**: Efficient parallel execution\n\n### Continuous Monitoring\n- [ ] **Performance Dashboard**: Real-time metrics collection\n- [ ] **Regression Testing**: Automated performance validation\n- [ ] **Trend Analysis**: Performance evolution tracking\n- [ ] **Alert System**: Immediate regression notification\n\n## Related V3 Skills\n\n- `v3-integration-deep` - Performance integration with agentic-flow\n- `v3-memory-unification` - Memory performance optimization\n- `v3-swarm-coordination` - Swarm performance coordination\n- `v3-security-overhaul` - Secure performance patterns\n\n## Usage Examples\n\n### Complete Performance Validation\n```bash\n# Full performance suite\nnpm run benchmark:v3\n\n# Specific target validation\nnpm run benchmark:flash-attention\nnpm run benchmark:agentdb-search\nnpm run benchmark:memory-optimization\n\n# Continuous monitoring\nnpm run monitor:performance\n```","# V3 Performance Optimization\n\n## What This Skill Does\n\nValidates and optimizes claude-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.\n\n## Quick Start\n\n```bash\n# Initialize performance optimization\nTask(\"Performance baseline\", \"Establish v2 performance benchmarks\", \"v3-performance-engineer\")\n\n# Target validation (parallel)\nTask(\"Flash Attention\", \"Validate 2.49x-7.47x speedup target\", \"v3-performance-engineer\")\nTask(\"Search optimization\", \"Validate 150x-12,500x search improvement\", \"v3-performance-engineer\")\nTask(\"Memory optimization\", \"Achieve 50-75% memory reduction\", \"v3-performance-engineer\")\n```\n\n## Performance Target Matrix\n\n### Flash Attention Revolution\n```\n┌─────────────────────────────────────────┐\n│           FLASH ATTENTION               │\n├─────────────────────────────────────────┤\n│  Baseline: Standard attention           │\n│  Target:   2.49x - 7.47x speedup       │\n│  Memory:   50-75% reduction             │\n│  Latency:  Sub-millisecond processing   │\n└─────────────────────────────────────────┘\n```\n\n### Search Performance Revolution\n```\n┌─────────────────────────────────────────┐\n│            SEARCH OPTIMIZATION         │\n├─────────────────────────────────────────┤\n│  Current:  O(n) linear search           │\n│  Target:   150x - 12,500x improvement   │\n│  Method:   HNSW indexing                │\n│  Latency:  \u003C100ms for 1M+ entries       │\n└─────────────────────────────────────────┘\n```\n\n## Comprehensive Benchmark Suite\n\n### Startup Performance\n```typescript\nclass StartupBenchmarks {\n  async benchmarkColdStart(): Promise\u003CBenchmarkResult> {\n    const startTime = performance.now();\n\n    await this.initializeCLI();\n    await this.initializeMCPServer();\n    await this.spawnTestAgent();\n\n    const totalTime = performance.now() - startTime;\n\n    return {\n      total: totalTime,\n      target: 500, \u002F\u002F ms\n      achieved: totalTime \u003C 500\n    };\n  }\n}\n```\n\n### Memory Operation Benchmarks\n```typescript\nclass MemoryBenchmarks {\n  async benchmarkVectorSearch(): Promise\u003CSearchBenchmark> {\n    const queries = this.generateTestQueries(10000);\n\n    \u002F\u002F Baseline: Current linear search\n    const baselineTime = await this.timeOperation(() =>\n      this.currentMemory.searchAll(queries)\n    );\n\n    \u002F\u002F Target: HNSW search\n    const hnswTime = await this.timeOperation(() =>\n      this.agentDBMemory.hnswSearchAll(queries)\n    );\n\n    const improvement = baselineTime \u002F hnswTime;\n\n    return {\n      baseline: baselineTime,\n      hnsw: hnswTime,\n      improvement,\n      targetRange: [150, 12500],\n      achieved: improvement >= 150\n    };\n  }\n\n  async benchmarkMemoryUsage(): Promise\u003CMemoryBenchmark> {\n    const baseline = process.memoryUsage().heapUsed;\n\n    await this.loadTestDataset();\n    const withData = process.memoryUsage().heapUsed;\n\n    await this.enableOptimization();\n    const optimized = process.memoryUsage().heapUsed;\n\n    const reduction = (withData - optimized) \u002F withData;\n\n    return {\n      baseline,\n      withData,\n      optimized,\n      reductionPercent: reduction * 100,\n      targetReduction: [50, 75],\n      achieved: reduction >= 0.5\n    };\n  }\n}\n```\n\n### Swarm Coordination Benchmarks\n```typescript\nclass SwarmBenchmarks {\n  async benchmark15AgentCoordination(): Promise\u003CSwarmBenchmark> {\n    const agents = await this.spawn15Agents();\n\n    \u002F\u002F Coordination latency\n    const coordinationTime = await this.timeOperation(() =>\n      this.coordinateSwarmTask(agents)\n    );\n\n    \u002F\u002F Task decomposition\n    const decompositionTime = await this.timeOperation(() =>\n      this.decomposeComplexTask()\n    );\n\n    \u002F\u002F Consensus achievement\n    const consensusTime = await this.timeOperation(() =>\n      this.achieveSwarmConsensus(agents)\n    );\n\n    return {\n      coordination: coordinationTime,\n      decomposition: decompositionTime,\n      consensus: consensusTime,\n      agentCount: 15,\n      efficiency: this.calculateEfficiency(agents)\n    };\n  }\n}\n```\n\n### Flash Attention Benchmarks\n```typescript\nclass AttentionBenchmarks {\n  async benchmarkFlashAttention(): Promise\u003CAttentionBenchmark> {\n    const sequences = this.generateSequences([512, 1024, 2048, 4096]);\n    const results = [];\n\n    for (const sequence of sequences) {\n      \u002F\u002F Baseline attention\n      const baselineResult = await this.benchmarkStandardAttention(sequence);\n\n      \u002F\u002F Flash attention\n      const flashResult = await this.benchmarkFlashAttention(sequence);\n\n      results.push({\n        sequenceLength: sequence.length,\n        speedup: baselineResult.time \u002F flashResult.time,\n        memoryReduction: (baselineResult.memory - flashResult.memory) \u002F baselineResult.memory,\n        targetSpeedup: [2.49, 7.47],\n        achieved: this.checkTarget(flashResult, [2.49, 7.47])\n      });\n    }\n\n    return {\n      results,\n      averageSpeedup: this.calculateAverage(results, 'speedup'),\n      averageMemoryReduction: this.calculateAverage(results, 'memoryReduction')\n    };\n  }\n}\n```\n\n### SONA Learning Benchmarks\n```typescript\nclass SONABenchmarks {\n  async benchmarkAdaptationTime(): Promise\u003CSONABenchmark> {\n    const scenarios = [\n      'pattern_recognition',\n      'task_optimization',\n      'error_correction',\n      'performance_tuning'\n    ];\n\n    const results = [];\n\n    for (const scenario of scenarios) {\n      const startTime = performance.hrtime.bigint();\n      await this.sona.adapt(scenario);\n      const endTime = performance.hrtime.bigint();\n\n      const adaptationTimeMs = Number(endTime - startTime) \u002F 1000000;\n\n      results.push({\n        scenario,\n        adaptationTime: adaptationTimeMs,\n        target: 0.05, \u002F\u002F ms\n        achieved: adaptationTimeMs \u003C= 0.05\n      });\n    }\n\n    return {\n      scenarios: results,\n      averageTime: results.reduce((sum, r) => sum + r.adaptationTime, 0) \u002F results.length,\n      successRate: results.filter(r => r.achieved).length \u002F results.length\n    };\n  }\n}\n```\n\n## Performance Monitoring Dashboard\n\n### Real-time Metrics\n```typescript\nclass PerformanceMonitor {\n  async collectMetrics(): Promise\u003CPerformanceSnapshot> {\n    return {\n      timestamp: Date.now(),\n      flashAttention: await this.measureFlashAttention(),\n      searchPerformance: await this.measureSearchSpeed(),\n      memoryUsage: await this.measureMemoryEfficiency(),\n      startupTime: await this.measureStartupLatency(),\n      sonaAdaptation: await this.measureSONASpeed(),\n      swarmCoordination: await this.measureSwarmEfficiency()\n    };\n  }\n\n  async generateReport(): Promise\u003CPerformanceReport> {\n    const snapshot = await this.collectMetrics();\n\n    return {\n      summary: this.generateSummary(snapshot),\n      achievements: this.checkTargetAchievements(snapshot),\n      trends: this.analyzeTrends(),\n      recommendations: this.generateOptimizations(),\n      regressions: await this.detectRegressions()\n    };\n  }\n}\n```\n\n### Continuous Regression Detection\n```typescript\nclass PerformanceRegression {\n  async detectRegressions(): Promise\u003CRegressionReport> {\n    const current = await this.runFullBenchmark();\n    const baseline = await this.getBaseline();\n\n    const regressions = [];\n\n    for (const [metric, currentValue] of Object.entries(current)) {\n      const baselineValue = baseline[metric];\n      const change = (currentValue - baselineValue) \u002F baselineValue;\n\n      if (change \u003C -0.05) { \u002F\u002F 5% regression threshold\n        regressions.push({\n          metric,\n          baseline: baselineValue,\n          current: currentValue,\n          regressionPercent: change * 100,\n          severity: this.classifyRegression(change)\n        });\n      }\n    }\n\n    return {\n      hasRegressions: regressions.length > 0,\n      regressions,\n      recommendations: this.generateRegressionFixes(regressions)\n    };\n  }\n}\n```\n\n## Optimization Strategies\n\n### Memory Optimization\n```typescript\nclass MemoryOptimization {\n  async optimizeMemoryUsage(): Promise\u003COptimizationResult> {\n    \u002F\u002F Implement memory pooling\n    await this.setupMemoryPools();\n\n    \u002F\u002F Enable garbage collection tuning\n    await this.optimizeGarbageCollection();\n\n    \u002F\u002F Implement object reuse patterns\n    await this.setupObjectPools();\n\n    \u002F\u002F Enable memory compression\n    await this.enableMemoryCompression();\n\n    return this.validateMemoryReduction();\n  }\n}\n```\n\n### CPU Optimization\n```typescript\nclass CPUOptimization {\n  async optimizeCPUUsage(): Promise\u003COptimizationResult> {\n    \u002F\u002F Implement worker thread pools\n    await this.setupWorkerThreads();\n\n    \u002F\u002F Enable CPU-specific optimizations\n    await this.enableSIMDInstructions();\n\n    \u002F\u002F Implement task batching\n    await this.optimizeTaskBatching();\n\n    return this.validateCPUImprovement();\n  }\n}\n```\n\n## Target Validation Framework\n\n### Performance Gates\n```typescript\nclass PerformanceGates {\n  async validateAllTargets(): Promise\u003CValidationReport> {\n    const results = await Promise.all([\n      this.validateFlashAttention(),     \u002F\u002F 2.49x-7.47x\n      this.validateSearchPerformance(),  \u002F\u002F 150x-12,500x\n      this.validateMemoryReduction(),    \u002F\u002F 50-75%\n      this.validateStartupTime(),        \u002F\u002F \u003C500ms\n      this.validateSONAAdaptation()      \u002F\u002F \u003C0.05ms\n    ]);\n\n    return {\n      allTargetsAchieved: results.every(r => r.achieved),\n      results,\n      overallScore: this.calculateOverallScore(results),\n      recommendations: this.generateRecommendations(results)\n    };\n  }\n}\n```\n\n## Success Metrics\n\n### Primary Targets\n- [ ] **Flash Attention**: 2.49x-7.47x speedup validated\n- [ ] **Search Performance**: 150x-12,500x improvement confirmed\n- [ ] **Memory Reduction**: 50-75% usage optimization achieved\n- [ ] **Startup Time**: \u003C500ms cold start consistently\n- [ ] **SONA Adaptation**: \u003C0.05ms learning response time\n- [ ] **15-Agent Coordination**: Efficient parallel execution\n\n### Continuous Monitoring\n- [ ] **Performance Dashboard**: Real-time metrics collection\n- [ ] **Regression Testing**: Automated performance validation\n- [ ] **Trend Analysis**: Performance evolution tracking\n- [ ] **Alert System**: Immediate regression notification\n\n## Related V3 Skills\n\n- `v3-integration-deep` - Performance integration with agentic-flow\n- `v3-memory-unification` - Memory performance optimization\n- `v3-swarm-coordination` - Swarm performance coordination\n- `v3-security-overhaul` - Secure performance patterns\n\n## Usage Examples\n\n### Complete Performance Validation\n```bash\n# Full performance suite\nnpm run benchmark:v3\n\n# Specific target validation\nnpm run benchmark:flash-attention\nnpm run benchmark:agentdb-search\nnpm run benchmark:memory-optimization\n\n# Continuous monitoring\nnpm run monitor:performance\n```",{"name":5,"description":7},"main","63caf799a0409377ce4fd434e24e9eac7c05c6fa",".claude\u002Fskills\u002Fv3-performance-optimization",0,"https:\u002F\u002Fgithub.com\u002Fmanykarim\u002Frobotframework-agentguard","https:\u002F\u002Fgithub.com\u002Fmanykarim","2026-07-27 14:20:37",{"source":8,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add manykarim\u002Frobotframework-agentguard --skill \"V3 Performance Optimization\"",{"tool":50,"command":51},"bunx","bunx skills add manykarim\u002Frobotframework-agentguard --skill \"V3 Performance Optimization\"",{"tool":53,"command":54},"pnpm","pnpm dlx skills add manykarim\u002Frobotframework-agentguard --skill \"V3 Performance 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