V3 Performance Optimization

ruvnet/ruflo/v3/@claude-flow/cli/.claude/skills/v3-performance-optimization

by ruvnet58e0ae7e14e68aab45a4127d6f42f567bbcfb328No license74K starsListed Oct 9, 2026Updated Oct 9, 2026Repository updated yesterday

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.

Instructions onlySoftware Development
AI-generated overview

Validates and optimizes claude-flow v3 performance targets through benchmarking and regression monitoring.

What it does
This skill guides an agent through validating and optimizing claude-flow v3 performance, covering Flash Attention speedup, AgentDB HNSW search indexing, memory reduction, startup latency, SONA adaptation and swarm coordination. It describes benchmark suites, a real-time performance monitoring dashboard, regression detection with thresholds, and optimization strategies for memory and CPU. It produces benchmark results, validation reports and optimization recommendations rather than executable code.
When to use it
Use it when establishing performance baselines, validating v3 speedup and memory targets, or setting up continuous performance monitoring and regression detection for claude-flow v3.
Requirements
Instructions only; no scripts are shipped. It references TypeScript benchmark classes and npm benchmark/monitor commands, so a claude-flow v3 environment with Node.js and those npm scripts is expected.

V3 Performance Optimization

What This Skill Does

Validates and optimizes claude-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.

Quick Start

bash
# Initialize performance optimizationTask("Performance baseline", "Establish v2 performance benchmarks", "v3-performance-engineer")
# Target validation (parallel)Task("Flash Attention", "Validate 2.49x-7.47x speedup target", "v3-performance-engineer")Task("Search optimization", "Validate 150x-12,500x search improvement", "v3-performance-engineer")Task("Memory optimization", "Achieve 50-75% memory reduction", "v3-performance-engineer")

Performance Target Matrix

Flash Attention Revolution

┌─────────────────────────────────────────┐│           FLASH ATTENTION               │├─────────────────────────────────────────┤│  Baseline: Standard attention           ││  Target:   2.49x - 7.47x speedup       ││  Memory:   50-75% reduction             ││  Latency:  Sub-millisecond processing   │└─────────────────────────────────────────┘

Search Performance Revolution

┌─────────────────────────────────────────┐│            SEARCH OPTIMIZATION         │├─────────────────────────────────────────┤│  Current:  O(n) linear search           ││  Target:   150x - 12,500x improvement   ││  Method:   HNSW indexing                ││  Latency:  <100ms for 1M+ entries       │└─────────────────────────────────────────┘

Comprehensive Benchmark Suite

Startup Performance

typescript
class StartupBenchmarks {  async benchmarkColdStart(): Promise<BenchmarkResult> {    const startTime = performance.now();
    await this.initializeCLI();    await this.initializeMCPServer();    await this.spawnTestAgent();
    const totalTime = performance.now() - startTime;
    return {      total: totalTime,      target: 500, // ms      achieved: totalTime < 500    };  }}

Memory Operation Benchmarks

typescript
class MemoryBenchmarks {  async benchmarkVectorSearch(): Promise<SearchBenchmark> {    const queries = this.generateTestQueries(10000);
    // Baseline: Current linear search    const baselineTime = await this.timeOperation(() =>      this.currentMemory.searchAll(queries)    );
    // Target: HNSW search    const hnswTime = await this.timeOperation(() =>      this.agentDBMemory.hnswSearchAll(queries)    );
    const improvement = baselineTime / hnswTime;
    return {      baseline: baselineTime,      hnsw: hnswTime,      improvement,      targetRange: [150, 12500],      achieved: improvement >= 150    };  }
  async benchmarkMemoryUsage(): Promise<MemoryBenchmark> {    const baseline = process.memoryUsage().heapUsed;
    await this.loadTestDataset();    const withData = process.memoryUsage().heapUsed;
    await this.enableOptimization();    const optimized = process.memoryUsage().heapUsed;
    const reduction = (withData - optimized) / withData;
    return {      baseline,      withData,      optimized,      reductionPercent: reduction * 100,      targetReduction: [50, 75],      achieved: reduction >= 0.5    };  }}

Swarm Coordination Benchmarks

typescript
class SwarmBenchmarks {  async benchmark15AgentCoordination(): Promise<SwarmBenchmark> {    const agents = await this.spawn15Agents();
    // Coordination latency    const coordinationTime = await this.timeOperation(() =>      this.coordinateSwarmTask(agents)    );
    // Task decomposition    const decompositionTime = await this.timeOperation(() =>      this.decomposeComplexTask()    );
    // Consensus achievement    const consensusTime = await this.timeOperation(() =>      this.achieveSwarmConsensus(agents)    );
    return {      coordination: coordinationTime,      decomposition: decompositionTime,      consensus: consensusTime,      agentCount: 15,      efficiency: this.calculateEfficiency(agents)    };  }}

Flash Attention Benchmarks

typescript
class AttentionBenchmarks {  async benchmarkFlashAttention(): Promise<AttentionBenchmark> {    const sequences = this.generateSequences([512, 1024, 2048, 4096]);    const results = [];
    for (const sequence of sequences) {      // Baseline attention      const baselineResult = await this.benchmarkStandardAttention(sequence);
      // Flash attention      const flashResult = await this.benchmarkFlashAttention(sequence);
      results.push({        sequenceLength: sequence.length,        speedup: baselineResult.time / flashResult.time,        memoryReduction: (baselineResult.memory - flashResult.memory) / baselineResult.memory,        targetSpeedup: [2.49, 7.47],        achieved: this.checkTarget(flashResult, [2.49, 7.47])      });    }
    return {      results,      averageSpeedup: this.calculateAverage(results, 'speedup'),      averageMemoryReduction: this.calculateAverage(results, 'memoryReduction')    };  }}

SONA Learning Benchmarks

typescript
class SONABenchmarks {  async benchmarkAdaptationTime(): Promise<SONABenchmark> {    const scenarios = [      'pattern_recognition',      'task_optimization',      'error_correction',      'performance_tuning'    ];
    const results = [];
    for (const scenario of scenarios) {      const startTime = performance.hrtime.bigint();      await this.sona.adapt(scenario);      const endTime = performance.hrtime.bigint();
      const adaptationTimeMs = Number(endTime - startTime) / 1000000;
      results.push({        scenario,        adaptationTime: adaptationTimeMs,        target: 0.05, // ms        achieved: adaptationTimeMs <= 0.05      });    }
    return {      scenarios: results,      averageTime: results.reduce((sum, r) => sum + r.adaptationTime, 0) / results.length,      successRate: results.filter(r => r.achieved).length / results.length    };  }}

Performance Monitoring Dashboard

Real-time Metrics

typescript
class PerformanceMonitor {  async collectMetrics(): Promise<PerformanceSnapshot> {    return {      timestamp: Date.now(),      flashAttention: await this.measureFlashAttention(),      searchPerformance: await this.measureSearchSpeed(),      memoryUsage: await this.measureMemoryEfficiency(),      startupTime: await this.measureStartupLatency(),      sonaAdaptation: await this.measureSONASpeed(),      swarmCoordination: await this.measureSwarmEfficiency()    };  }
  async generateReport(): Promise<PerformanceReport> {    const snapshot = await this.collectMetrics();
    return {      summary: this.generateSummary(snapshot),      achievements: this.checkTargetAchievements(snapshot),      trends: this.analyzeTrends(),      recommendations: this.generateOptimizations(),      regressions: await this.detectRegressions()    };  }}

Continuous Regression Detection

typescript
class PerformanceRegression {  async detectRegressions(): Promise<RegressionReport> {    const current = await this.runFullBenchmark();    const baseline = await this.getBaseline();
    const regressions = [];
    for (const [metric, currentValue] of Object.entries(current)) {      const baselineValue = baseline[metric];      const change = (currentValue - baselineValue) / baselineValue;
      if (change < -0.05) { // 5% regression threshold        regressions.push({          metric,          baseline: baselineValue,          current: currentValue,          regressionPercent: change * 100,          severity: this.classifyRegression(change)        });      }    }
    return {      hasRegressions: regressions.length > 0,      regressions,      recommendations: this.generateRegressionFixes(regressions)    };  }}

Optimization Strategies

Memory Optimization

typescript
class MemoryOptimization {  async optimizeMemoryUsage(): Promise<OptimizationResult> {    // Implement memory pooling    await this.setupMemoryPools();
    // Enable garbage collection tuning    await this.optimizeGarbageCollection();
    // Implement object reuse patterns    await this.setupObjectPools();
    // Enable memory compression    await this.enableMemoryCompression();
    return this.validateMemoryReduction();  }}

CPU Optimization

typescript
class CPUOptimization {  async optimizeCPUUsage(): Promise<OptimizationResult> {    // Implement worker thread pools    await this.setupWorkerThreads();
    // Enable CPU-specific optimizations    await this.enableSIMDInstructions();
    // Implement task batching    await this.optimizeTaskBatching();
    return this.validateCPUImprovement();  }}

Target Validation Framework

Performance Gates

typescript
class PerformanceGates {  async validateAllTargets(): Promise<ValidationReport> {    const results = await Promise.all([      this.validateFlashAttention(),     // 2.49x-7.47x      this.validateSearchPerformance(),  // 150x-12,500x      this.validateMemoryReduction(),    // 50-75%      this.validateStartupTime(),        // <500ms      this.validateSONAAdaptation()      // <0.05ms    ]);
    return {      allTargetsAchieved: results.every(r => r.achieved),      results,      overallScore: this.calculateOverallScore(results),      recommendations: this.generateRecommendations(results)    };  }}

Success Metrics

Primary Targets

  • Flash Attention: 2.49x-7.47x speedup validated
  • Search Performance: 150x-12,500x improvement confirmed
  • Memory Reduction: 50-75% usage optimization achieved
  • Startup Time: <500ms cold start consistently
  • SONA Adaptation: <0.05ms learning response time
  • 15-Agent Coordination: Efficient parallel execution

Continuous Monitoring

  • Performance Dashboard: Real-time metrics collection
  • Regression Testing: Automated performance validation
  • Trend Analysis: Performance evolution tracking
  • Alert System: Immediate regression notification

Related V3 Skills

  • v3-integration-deep - Performance integration with agentic-flow
  • v3-memory-unification - Memory performance optimization
  • v3-swarm-coordination - Swarm performance coordination
  • v3-security-overhaul - Secure performance patterns

Usage Examples

Complete Performance Validation

bash
# Full performance suitenpm run benchmark:v3
# Specific target validationnpm run benchmark:flash-attentionnpm run benchmark:agentdb-searchnpm run benchmark:memory-optimization
# Continuous monitoringnpm run monitor:performance

Source and attribution

Source:ruvnet/rufloinv3/@claude-flow/cli/.claude/skills/v3-performance-optimizationat commit58e0ae7

License: No license

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