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Comprehensive benchmarking and optimization suite.","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Agent-智能体\u002FYYC3-Agent-智能体-ruflo\u002Fv3\u002F@claude-flow\u002Fcli\u002F.claude\u002Fskills\u002Fv3-performance-optimization\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-Agent-智能体\u002FYYC3-Agent-智能体-ruflo\u002Fv3\u002F@claude-flow\u002Fcli\u002F.claude\u002Fskills\u002Fv3-performance-optimization",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill \"V3 Performance Optimization\"",{"tool":39,"command":40},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill \"V3 Performance Optimization\"",{"tool":42,"command":43},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill \"V3 Performance Optimization\"","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Agent-智能体\u002FYYC3-Agent-智能体-ruflo\u002Fv3\u002F@claude-flow\u002Fcli\u002F.claude\u002Fskills\u002Fv3-performance-optimization\u002FSKILL.md",true,[],[48,55,62,69,76,83],{"skillNo":49,"name":50,"nameZh":6,"description":51,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include \"Vercel Sandbox browser\", \"microVM Chrome\", \"agent-browser in sandbox\", \"browser automation on Vercel\", or any task requiring Chrome in a Vercel Sandbox.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fvercel-sandbox\u002FSKILL.md",[],[],{"skillNo":56,"name":57,"nameZh":6,"description":58,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":59,"categories":60,"keywords":61,"publishedAt":19},"01M7CTD8D2BNJY54S65M47HEJA","slack","Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":63,"name":64,"nameZh":6,"description":65,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":66,"categories":67,"keywords":68,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include \"automate Slack app\", \"control VS Code\", \"interact with Discord app\", \"test this Electron app\", \"connect to desktop app\", or any task requiring automation of a native Electron application.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Felectron\u002FSKILL.md",[],[],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":19},"01M7CTCBJY76688V4DCZGWFHGQ","dogfood","Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to \"dogfood\", \"QA\", \"exploratory test\", \"find issues\", \"bug hunt\", \"test this app\u002Fsite\u002Fplatform\", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fdogfood\u002FSKILL.md",[],[],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":19},"01M7CTC4H0FFGVEAQR9PA4GA1A","agent-browser","Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to \"open a website\", \"fill out a form\", \"click a button\", \"take a screenshot\", \"scrape data from a page\", \"test this web app\", \"login to a site\", \"automate browser actions\", or any task requiring programmatic web interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fagent-browser\u002FSKILL.md",[],[],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":19},"01M7CTBKVFYBG78FDSABSDJTTQ","shadcn","Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn\u002Fui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for \"shadcn init\", \"create an app with --preset\", \"switch to --preset\", and for building a design system or applying a DESIGN.md or brand spec to shadcn\u002Fui.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-UI-界面组件\u002FYYC3-UI-界面组件-ui\u002Fskills\u002Fshadcn\u002FSKILL.md",[],[]]