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agent-performance-benchmarker

Benchmark distributed consensus protocols, methods computers use to agree on shared results. Compare processing rates, response times, scaling behavior, and resource usage across Byzantine, Raft, and Gossip, then identify bottlenecks and potential tuning changes.

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원문원문 설명 보기

Agent skill for performance-benchmarker - invoke with $agent-performance-benchmarker

ruvnet/ruflo

설치 방법 보기

이럴 때 유용합니다

Comparing Byzantine, Raft, and Gossip performance
Measuring response times under increasing load
Finding CPU, memory, or network bottlenecks
Evaluating scaling and fault tolerance

포함된 파일(1)

SKILL.md26.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

name: performance-benchmarker type: analyst color: "#607D8B" description: Implements comprehensive performance benchmarking for distributed consensus protocols capabilities:

throughput_measurement
latency_analysis
resource_monitoring
comparative_analysis
adaptive_tuning priority: medium hooks: pre: | echo "📊 Performance Benchmarker analyzing: $TASK"
Initialize monitoring systems
if [[ "$TASK" == "benchmark" ]]; then echo "⚡ Starting performance metric collection" fi post: | echo "📈 Performance analysis complete"
Generate performance report
echo "📋 Compiling benchmarking results and recommendations"

Performance Benchmarker

Implements comprehensive performance benchmarking and optimization analysis for distributed consensus protocols.

Core Responsibilities

Protocol Benchmarking: Measure throughput, latency, and scalability across consensus algorithms
Resource Monitoring: Track CPU, memory, network, and storage utilization patterns
Comparative Analysis: Compare Byzantine, Raft, and Gossip protocol performance
Adaptive Tuning: Implement real-time parameter optimization and load balancing
Performance Reporting: Generate actionable insights and optimization recommendations

Technical Implementation

Core Benchmarking Framework

class ConsensusPerformanceBenchmarker {
  constructor() {
    this.benchmarkSuites = new Map();
    this.performanceMetrics = new Map();
    this.historicalData = new TimeSeriesDatabase();
    this.currentBenchmarks = new Set();
    this.adaptiveOptimizer = new AdaptiveOptimizer();
    this.alertSystem = new PerformanceAlertSystem();
  }

  // Register benchmark suite for specific consensus protocol
  registerBenchmarkSuite(protocolName, benchmarkConfig) {
    const suite = new BenchmarkSuite(protocolName, benchmarkConfig);
    this.benchmarkSuites.set(protocolName, suite);
    
    return suite;
  }

  // Execute comprehensive performance benchmarks
  async runComprehensiveBenchmarks(protocols, scenarios) {
    const results = new Map();
    
    for (const protocol of protocols) {
      const protocolResults = new Map();
      
      for (const scenario of scenarios) {
        console.log(`Running ${scenario.name} benchmark for ${protocol}`);
        
        const benchmarkResult = await this.executeBenchmarkScenario(
          protocol, scenario
        );
        
        protocolResults.set(scenario.name, benchmarkResult);
        
        // Store in historical database
        await this.historicalData.store({
          protocol: protocol,
          scenario: scenario.name,
          timestamp: Date.now(),
          metrics: benchmarkResult
        });
      }
      
      results.set(protocol, protocolResults);
    }
    
    // Generate comparative analysis
    const analysis = await this.generateComparativeAnalysis(results);
    
    // Trigger adaptive optimizations
    await this.adaptiveOptimizer.optimizeBasedOnResults(results);
    
    return {
      benchmarkResults: results,
      comparativeAnalysis: analysis,
      recommendations: await this.generateOptimizationRecommendations(results)
    };
  }

  async executeBenchmarkScenario(protocol, scenario) {
    const benchmark = this.benchmarkSuites.get(protocol);
    if (!benchmark) {
      throw new Error(`No benchmark suite found for protocol: ${protocol}`);
    }

    // Initialize benchmark environment
    const environment = await this.setupBenchmarkEnvironment(scenario);
    
    try {
      // Pre-benchmark setup
      await benchmark.setup(environment);
      
      // Execute benchmark phases
      const results = {
        throughput: await this.measureThroughput(benchmark, scenario),
        latency: await this.measureLatency(benchmark, scenario),
        resourceUsage: await this.measureResourceUsage(benchmark, scenario),
        scalability: await this.measureScalability(benchmark, scenario),
        faultTolerance: await this.measureFaultTolerance(benchmark, scenario)
      };
      
      // Post-benchmark analysis
      results.analysis = await this.analyzeBenchmarkResults(results);
      
      return results;
      
    } finally {
      // Cleanup benchmark environment
      await this.cleanupBenchmarkEnvironment(environment);
    }
  }
}

Throughput Measurement System

class ThroughputBenchmark {
  constructor(protocol, configuration) {
    this.protocol = protocol;
    this.config = configuration;
    this.metrics = new MetricsCollector();
    this.loadGenerator = new LoadGenerator();
  }

  async measureThroughput(scenario) {
    const measurements = [];
    const duration = scenario.duration || 60000; // 1 minute default
    const startTime = Date.now();
    
    // Initialize load generator
    await this.loadGenerator.initialize({
      requestRate: scenario.initialRate || 10,
      rampUp: scenario.rampUp || false,
      pattern: scenario.pattern || 'constant'
    });
    
    // Start metrics collection
    this.metrics.startCollection(['transactions_per_second', 'success_rate']);
    
    let currentRate = scenario.initialRate || 10;
    const rateIncrement = scenario.rateIncrement || 5;
    const measurementInterval = 5000; // 5 seconds
    
    while (Date.now() - startTime < duration) {
      const intervalStart = Date.now();
      
      // Generate load for this interval
      const transactions = await this.generateTransactionLoad(
        currentRate, measurementInterval
      );
      
      // Measure throughput for this interval
      const intervalMetrics = await this.measureIntervalThroughput(
        transactions, measurementInterval
      );
      
      measurements.push({
        timestamp: intervalStart,
        requestRate: currentRate,
        actualThroughput: intervalMetrics.throughput,
        successRate: intervalMetrics.successRate,
        averageLatency: intervalMetrics.averageLatency,
        p95Latency: intervalMetrics.p95Latency,
        p99Latency: intervalMetrics.p99Latency
      });
      
      // Adaptive rate adjustment
      if (scenario.rampUp && intervalMetrics.successRate > 0.95) {
        currentRate += rateIncrement;
      } else if (intervalMetrics.successRate < 0.8) {
        currentRate = Math.max(1, currentRate - rateIncrement);
      }
      
      // Wait for next interval
      const elapsed = Date.now() - intervalStart;
      if (elapsed < measurementInterval) {
        await this.sleep(measurementInterval - elapsed);
      }
    }
    
    // Stop metrics collection
    this.metrics.stopCollection();
    
    // Analyze throughput results
    return this.analyzeThroughputMeasurements(measurements);
  }

  async generateTransactionLoad(rate, duration) {
    const transactions = [];
    const interval = 1000 / rate; // Interval between transactions in ms
    const endTime = Date.now() + duration;
    
    while (Date.now() < endTime) {
      const transactionStart = Date.now();
      
      const transaction = {
        id: `tx_${Date.now()}_${Math.random()}`,
        type: this.getRandomTransactionType(),
        data: this.generateTransactionData(),
        timestamp: transactionStart
      };
      
      // Submit transaction to consensus protocol
      const promise = this.protocol.submitTransaction(transaction)
        .then(result => ({
          ...transaction,
          result: result,
          latency: Date.now() - transactionStart,
          success: result.committed === true
        }))
        .catch(error => ({
          ...transaction,
          error: error,
          latency: Date.now() - transactionStart,
          success: false
        }));
      
      transactions.push(promise);
      
      // Wait for next transaction interval
      await this.sleep(interval);
    }
    
    // Wait for all transactions to complete
    return await Promise.all(transactions);
  }

  analyzeThroughputMeasurements(measurements) {
    const totalMeasurements = measurements.length;
    const avgThroughput = measurements.reduce((sum, m) => sum + m.actualThroughput, 0) / totalMeasurements;
    const maxThroughput = Math.max(...measurements.map(m => m.actualThroughput));
    const avgSuccessRate = measurements.reduce((sum, m) => sum + m.successRate, 0) / totalMeasurements;
    
    // Find optimal operating point (highest throughput with >95% success rate)
    const optimalPoints = measurements.filter(m => m.successRate >= 0.95);
    const optimalThroughput = optimalPoints.length > 0 ? 
      Math.max(...optimalPoints.map(m => m.actualThroughput)) : 0;
    
    return {
      averageThroughput: avgThroughput,
      maxThroughput: maxThroughput,
      optimalThroughput: optimalThroughput,
      averageSuccessRate: avgSuccessRate,
      measurements: measurements,
      sustainableThroughput: this.calculateSustainableThroughput(measurements),
      throughputVariability: this.calculateThroughputVariability(measurements)
    };
  }

  calculateSustainableThroughput(measurements) {
    // Find the highest throughput that can be sustained for >80% of the time
    const sortedThroughputs = measurements.map(m => m.actualThroughput).sort((a, b) => b - a);
    const p80Index = Math.floor(sortedThroughputs.length * 0.2);
    return sortedThroughputs[p80Index];
  }
}

Latency Analysis System

class LatencyBenchmark {
  constructor(protocol, configuration) {
    this.protocol = protocol;
    this.config = configuration;
    this.latencyHistogram = new LatencyHistogram();
    this.percentileCalculator = new PercentileCalculator();
  }

  async measureLatency(scenario) {
    const measurements = [];
    const sampleSize = scenario.sampleSize || 10000;
    const warmupSize = scenario.warmupSize || 1000;
    
    console.log(`Measuring latency with ${sampleSize} samples (${warmupSize} warmup)`);
    
    // Warmup phase
    await this.performWarmup(warmupSize);
    
    // Measurement phase
    for (let i = 0; i < sampleSize; i++) {
      const latencyMeasurement = await this.measureSingleTransactionLatency();
      measurements.push(latencyMeasurement);
      
      // Progress reporting
      if (i % 1000 === 0) {
        console.log(`Completed ${i}/${sampleSize} latency measurements`);
      }
    }
    
    // Analyze latency distribution
    return this.analyzeLatencyDistribution(measurements);
  }

  async measureSingleTransactionLatency() {
    const transaction = {
      id: `latency_tx_${Date.now()}_${Math.random()}`,
      type: 'benchmark',
      data: { value: Math.random() },
      phases: {}
    };
    
    // Phase 1: Submission
    const submissionStart = performance.now();
    const submissionPromise = this.protocol.submitTransaction(transaction);
    transaction.phases.submission = performance.now() - submissionStart;
    
    // Phase 2: Consensus
    const consensusStart = performance.now();
    const result = await submissionPromise;
    transaction.phases.consensus = performance.now() - consensusStart;
    
    // Phase 3: Application (if applicable)
    let applicationLatency = 0;
    if (result.applicationTime) {
      applicationLatency = result.applicationTime;
    }
    transaction.phases.application = applicationLatency;
    
    // Total end-to-end latency
    const totalLatency = transaction.phases.submission + 
                        transaction.phases.consensus + 
                        transaction.phases.application;
    
    return {
      transactionId: transaction.id,
      totalLatency: totalLatency,
      phases: transaction.phases,
      success: result.committed === true,
      timestamp: Date.now()
    };
  }

  analyzeLatencyDistribution(measurements) {
    const successfulMeasurements = measurements.filter(m => m.success);
    const latencies = successfulMeasurements.map(m => m.totalLatency);
    
    if (latencies.length === 0) {
      throw new Error('No successful latency measurements');
    }
    
    // Calculate percentiles
    const percentiles = this.percentileCalculator.calculate(latencies, [
      50, 75, 90, 95, 99, 99.9, 99.99
    ]);
    
    // Phase-specific analysis
    const phaseAnalysis = this.analyzePhaseLatencies(successfulMeasurements);
    
    // Latency distribution analysis
    const distribution = this.analyzeLatencyHistogram(latencies);
    
    return {
      sampleSize: successfulMeasurements.length,
      mean: latencies.reduce((sum, l) => sum + l, 0) / latencies.length,
      median: percentiles[50],
      standardDeviation: this.calculateStandardDeviation(latencies),
      percentiles: percentiles,
      phaseAnalysis: phaseAnalysis,
      distribution: distribution,
      outliers: this.identifyLatencyOutliers(latencies)
    };
  }

  analyzePhaseLatencies(measurements) {
    const phases = ['submission', 'consensus', 'application'];
    const phaseAnalysis = {};
    
    for (const phase of phases) {
      const phaseLatencies = measurements.map(m => m.phases[phase]);
      const validLatencies = phaseLatencies.filter(l => l > 0);
      
      if (validLatencies.length > 0) {
        phaseAnalysis[phase] = {
          mean: validLatencies.reduce((sum, l) => sum + l, 0) / validLatencies.length,
          p50: this.percentileCalculator.calculate(validLatencies, [50])[50],
          p95: this.percentileCalculator.calculate(validLatencies, [95])[95],
          p99: this.percentileCalculator.calculate(validLatencies, [99])[99],
          max: Math.max(...validLatencies),
          contributionPercent: (validLatencies.reduce((sum, l) => sum + l, 0) / 
                               measurements.reduce((sum, m) => sum + m.totalLatency, 0)) * 100
        };
      }
    }
    
    return phaseAnalysis;
  }
}

Resource Usage Monitor

class ResourceUsageMonitor {
  constructor() {
    this.monitoringActive = false;
    this.samplingInterval = 1000; // 1 second
    this.measurements = [];
    this.systemMonitor = new SystemMonitor();
  }

  async measureResourceUsage(protocol, scenario) {
    console.log('Starting resource usage monitoring');
    
    this.monitoringActive = true;
    this.measurements = [];
    
    // Start monitoring in background
    const monitoringPromise = this.startContinuousMonitoring();
    
    try {
      // Execute the benchmark scenario
      const benchmarkResult = await this.executeBenchmarkWithMonitoring(
        protocol, scenario
      );
      
      // Stop monitoring
      this.monitoringActive = false;
      await monitoringPromise;
      
      // Analyze resource usage
      const resourceAnalysis = this.analyzeResourceUsage();
      
      return {
        benchmarkResult: benchmarkResult,
        resourceUsage: resourceAnalysis
      };
      
    } catch (error) {
      this.monitoringActive = false;
      throw error;
    }
  }

  async startContinuousMonitoring() {
    while (this.monitoringActive) {
      const measurement = await this.collectResourceMeasurement();
      this.measurements.push(measurement);
      
      await this.sleep(this.samplingInterval);
    }
  }

  async collectResourceMeasurement() {
    const timestamp = Date.now();
    
    // CPU usage
    const cpuUsage = await this.systemMonitor.getCPUUsage();
    
    // Memory usage
    const memoryUsage = await this.systemMonitor.getMemoryUsage();
    
    // Network I/O
    const networkIO = await this.systemMonitor.getNetworkIO();
    
    // Disk I/O
    const diskIO = await this.systemMonitor.getDiskIO();
    
    // Process-specific metrics
    const processMetrics = await this.systemMonitor.getProcessMetrics();
    
    return {
      timestamp: timestamp,
      cpu: {
        totalUsage: cpuUsage.total,
        consensusUsage: cpuUsage.process,
        loadAverage: cpuUsage.loadAverage,
        coreUsage: cpuUsage.cores
      },
      memory: {
        totalUsed: memoryUsage.used,
        totalAvailable: memoryUsage.available,
        processRSS: memoryUsage.processRSS,
        processHeap: memoryUsage.processHeap,
        gcStats: memoryUsage.gcStats
      },
      network: {
        bytesIn: networkIO.bytesIn,
        bytesOut: networkIO.bytesOut,
        packetsIn: networkIO.packetsIn,
        packetsOut: networkIO.packetsOut,
        connectionsActive: networkIO.connectionsActive
      },
      disk: {
        bytesRead: diskIO.bytesRead,
        bytesWritten: diskIO.bytesWritten,
        operationsRead: diskIO.operationsRead,
        operationsWrite: diskIO.operationsWrite,
        queueLength: diskIO.queueLength
      },
      process: {
        consensusThreads: processMetrics.consensusThreads,
        fileDescriptors: processMetrics.fileDescriptors,
        uptime: processMetrics.uptime
      }
    };
  }

  analyzeResourceUsage() {
    if (this.measurements.length === 0) {
      return null;
    }
    
    const cpuAnalysis = this.analyzeCPUUsage();
    const memoryAnalysis = this.analyzeMemoryUsage();
    const networkAnalysis = this.analyzeNetworkUsage();
    const diskAnalysis = this.analyzeDiskUsage();
    
    return {
      duration: this.measurements[this.measurements.length - 1].timestamp - 
               this.measurements[0].timestamp,
      sampleCount: this.measurements.length,
      cpu: cpuAnalysis,
      memory: memoryAnalysis,
      network: networkAnalysis,
      disk: diskAnalysis,
      efficiency: this.calculateResourceEfficiency(),
      bottlenecks: this.identifyResourceBottlenecks()
    };
  }

  analyzeCPUUsage() {
    const cpuUsages = this.measurements.map(m => m.cpu.consensusUsage);
    
    return {
      average: cpuUsages.reduce((sum, usage) => sum + usage, 0) / cpuUsages.length,
      peak: Math.max(...cpuUsages),
      p95: this.calculatePercentile(cpuUsages, 95),
      variability: this.calculateStandardDeviation(cpuUsages),
      coreUtilization: this.analyzeCoreUtilization(),
      trends: this.analyzeCPUTrends()
    };
  }

  analyzeMemoryUsage() {
    const memoryUsages = this.measurements.map(m => m.memory.processRSS);
    const heapUsages = this.measurements.map(m => m.memory.processHeap);
    
    return {
      averageRSS: memoryUsages.reduce((sum, usage) => sum + usage, 0) / memoryUsages.length,
      peakRSS: Math.max(...memoryUsages),
      averageHeap: heapUsages.reduce((sum, usage) => sum + usage, 0) / heapUsages.length,
      peakHeap: Math.max(...heapUsages),
      memoryLeaks: this.detectMemoryLeaks(),
      gcImpact: this.analyzeGCImpact(),
      growth: this.calculateMemoryGrowth()
    };
  }

  identifyResourceBottlenecks() {
    const bottlenecks = [];
    
    // CPU bottleneck detection
    const avgCPU = this.measurements.reduce((sum, m) => sum + m.cpu.consensusUsage, 0) / 
                   this.measurements.length;
    if (avgCPU > 80) {
      bottlenecks.push({
        type: 'CPU',
        severity: 'HIGH',
        description: `High CPU usage (${avgCPU.toFixed(1)}%)`
      });
    }
    
    // Memory bottleneck detection
    const memoryGrowth = this.calculateMemoryGrowth();
    if (memoryGrowth.rate > 1024 * 1024) { // 1MB$s growth
      bottlenecks.push({
        type: 'MEMORY',
        severity: 'MEDIUM',
        description: `High memory growth rate (${(memoryGrowth.rate / 1024 / 1024).toFixed(2)} MB$s)`
      });
    }
    
    // Network bottleneck detection
    const avgNetworkOut = this.measurements.reduce((sum, m) => sum + m.network.bytesOut, 0) / 
                          this.measurements.length;
    if (avgNetworkOut > 100 * 1024 * 1024) { // 100 MB$s
      bottlenecks.push({
        type: 'NETWORK',
        severity: 'MEDIUM',
        description: `High network output (${(avgNetworkOut / 1024 / 1024).toFixed(2)} MB$s)`
      });
    }
    
    return bottlenecks;
  }
}

Adaptive Performance Optimizer

class AdaptiveOptimizer {
  constructor() {
    this.optimizationHistory = new Map();
    this.performanceModel = new PerformanceModel();
    this.parameterTuner = new ParameterTuner();
    this.currentOptimizations = new Map();
  }

  async optimizeBasedOnResults(benchmarkResults) {
    const optimizations = [];
    
    for (const [protocol, results] of benchmarkResults) {
      const protocolOptimizations = await this.optimizeProtocol(protocol, results);
      optimizations.push(...protocolOptimizations);
    }
    
    // Apply optimizations gradually
    await this.applyOptimizations(optimizations);
    
    return optimizations;
  }

  async optimizeProtocol(protocol, results) {
    const optimizations = [];
    
    // Analyze performance bottlenecks
    const bottlenecks = this.identifyPerformanceBottlenecks(results);
    
    for (const bottleneck of bottlenecks) {
      const optimization = await this.generateOptimization(protocol, bottleneck);
      if (optimization) {
        optimizations.push(optimization);
      }
    }
    
    // Parameter tuning based on performance characteristics
    const parameterOptimizations = await this.tuneParameters(protocol, results);
    optimizations.push(...parameterOptimizations);
    
    return optimizations;
  }

  identifyPerformanceBottlenecks(results) {
    const bottlenecks = [];
    
    // Throughput bottlenecks
    for (const [scenario, result] of results) {
      if (result.throughput && result.throughput.optimalThroughput < result.throughput.maxThroughput * 0.8) {
        bottlenecks.push({
          type: 'THROUGHPUT_DEGRADATION',
          scenario: scenario,
          severity: 'HIGH',
          impact: (result.throughput.maxThroughput - result.throughput.optimalThroughput) / 
                 result.throughput.maxThroughput,
          details: result.throughput
        });
      }
      
      // Latency bottlenecks
      if (result.latency && result.latency.p99 > result.latency.p50 * 10) {
        bottlenecks.push({
          type: 'LATENCY_TAIL',
          scenario: scenario,
          severity: 'MEDIUM',
          impact: result.latency.p99 / result.latency.p50,
          details: result.latency
        });
      }
      
      // Resource bottlenecks
      if (result.resourceUsage && result.resourceUsage.bottlenecks.length > 0) {
        bottlenecks.push({
          type: 'RESOURCE_CONSTRAINT',
          scenario: scenario,
          severity: 'HIGH',
          details: result.resourceUsage.bottlenecks
        });
      }
    }
    
    return bottlenecks;
  }

  async generateOptimization(protocol, bottleneck) {
    switch (bottleneck.type) {
      case 'THROUGHPUT_DEGRADATION':
        return await this.optimizeThroughput(protocol, bottleneck);
      case 'LATENCY_TAIL':
        return await this.optimizeLatency(protocol, bottleneck);
      case 'RESOURCE_CONSTRAINT':
        return await this.optimizeResourceUsage(protocol, bottleneck);
      default:
        return null;
    }
  }

  async optimizeThroughput(protocol, bottleneck) {
    const optimizations = [];
    
    // Batch size optimization
    if (protocol === 'raft') {
      optimizations.push({
        type: 'PARAMETER_ADJUSTMENT',
        parameter: 'max_batch_size',
        currentValue: await this.getCurrentParameter(protocol, 'max_batch_size'),
        recommendedValue: this.calculateOptimalBatchSize(bottleneck.details),
        expectedImprovement: '15-25% throughput increase',
        confidence: 0.8
      });
    }
    
    // Pipelining optimization
    if (protocol === 'byzantine') {
      optimizations.push({
        type: 'FEATURE_ENABLE',
        feature: 'request_pipelining',
        description: 'Enable request pipelining to improve throughput',
        expectedImprovement: '20-30% throughput increase',
        confidence: 0.7
      });
    }
    
    return optimizations.length > 0 ? optimizations[0] : null;
  }

  async tuneParameters(protocol, results) {
    const optimizations = [];
    
    // Use machine learning model to suggest parameter values
    const parameterSuggestions = await this.performanceModel.suggestParameters(
      protocol, results
    );
    
    for (const suggestion of parameterSuggestions) {
      if (suggestion.confidence > 0.6) {
        optimizations.push({
          type: 'PARAMETER_TUNING',
          parameter: suggestion.parameter,
          currentValue: suggestion.currentValue,
          recommendedValue: suggestion.recommendedValue,
          expectedImprovement: suggestion.expectedImprovement,
          confidence: suggestion.confidence,
          rationale: suggestion.rationale
        });
      }
    }
    
    return optimizations;
  }

  async applyOptimizations(optimizations) {
    // Sort by confidence and expected impact
    const sortedOptimizations = optimizations.sort((a, b) => 
      (b.confidence * parseFloat(b.expectedImprovement)) - 
      (a.confidence * parseFloat(a.expectedImprovement))
    );
    
    // Apply optimizations gradually
    for (const optimization of sortedOptimizations) {
      try {
        await this.applyOptimization(optimization);
        
        // Wait and measure impact
        await this.sleep(30000); // 30 seconds
        const impact = await this.measureOptimizationImpact(optimization);
        
        if (impact.improvement < 0.05) {
          // Revert if improvement is less than 5%
          await this.revertOptimization(optimization);
        } else {
          // Keep optimization and record success
          this.recordOptimizationSuccess(optimization, impact);
        }
        
      } catch (error) {
        console.error(`Failed to apply optimization:`, error);
        await this.revertOptimization(optimization);
      }
    }
  }
}

MCP Integration Hooks

Performance Metrics Storage

// Store comprehensive benchmark results
await this.mcpTools.memory_usage({
  action: 'store',
  key: `benchmark_results_${protocol}_${Date.now()}`,
  value: JSON.stringify({
    protocol: protocol,
    timestamp: Date.now(),
    throughput: throughputResults,
    latency: latencyResults,
    resourceUsage: resourceResults,
    optimizations: appliedOptimizations
  }),
  namespace: 'performance_benchmarks',
  ttl: 604800000 // 7 days
});

// Real-time performance monitoring
await this.mcpTools.metrics_collect({
  components: [
    'consensus_throughput',
    'consensus_latency_p99',
    'cpu_utilization',
    'memory_usage',
    'network_io_rate'
  ]
});

Neural Performance Learning

// Learn performance optimization patterns
await this.mcpTools.neural_patterns({
  action: 'learn',
  operation: 'performance_optimization',
  outcome: JSON.stringify({
    optimizationType: optimization.type,
    performanceGain: measurementResults.improvement,
    resourceImpact: measurementResults.resourceDelta,
    networkConditions: currentNetworkState
  })
});

// Predict optimal configurations
const configPrediction = await this.mcpTools.neural_predict({
  modelId: 'consensus_performance_model',
  input: JSON.stringify({
    workloadPattern: currentWorkload,
    networkTopology: networkState,
    resourceConstraints: systemResources
  })
});

This Performance Benchmarker provides comprehensive performance analysis, optimization recommendations, and adaptive tuning capabilities for distributed consensus protocols.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

같은 저장소의 스킬

add-model-descriptions

무료

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Adding newly available router models to chat-ui
Writing concise descriptions of model capabilities
Updating production and development model entries together

ruvnet/ruflo7.7만2026년 10월 18일 업데이트

adr-create

무료

Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Record the reasoning behind adopting a technology
Start an ADR for an API or data model change
Document an infrastructure decision

ruvnet/ruflo7.7만2026년 10월 18일 업데이트

adr-index

무료

Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Index ADRs imported from another project
Refresh a graph that differs from files on disk
Set up ADR tracking in an existing codebase

ruvnet/ruflo7.7만2026년 10월 18일 업데이트

adr-reindex

무료

Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Rebuild the index after deleting an ADR file
Remove stale relationships after editing an ADR
Reconcile the ADR index on a schedule

ruvnet/ruflo7.7만2026년 10월 18일 업데이트

adr-review

무료

Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Check a pull request before merging
Review major code changes against agreed design decisions
Find references to superseded ADRs

ruvnet/ruflo7.7만2026년 10월 18일 업데이트

adr-verify

무료

Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Checking ADR integrity after import
Blocking CI when ADR issues are found
Reviewing ADR data before a release

ruvnet/ruflo7.7만2026년 10월 18일 업데이트

ruvnet의 스킬 모두 보기

이 스킬의 문제 신고

설치

명령으로 설치

npx skills add https://github.com/ruvnet/ruflo --skill agent-performance-benchmarker

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-performance-benchmarker

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/agent-performance-benchmarker/, Codex는 ~/.agents/skills/agent-performance-benchmarker/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: ruvnet/ruflo
스타: 76,638
포크: 9,061
카테고리: 테스트·보안
라이선스: MIT
원문 언어: 영어
최종 업데이트: 2026년 10월 18일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.

이럴 때 유용합니다

포함된 파일(1)

SKILL.md(원문)

Initialize monitoring systems

Generate performance report

Performance Benchmarker

Core Responsibilities

Technical Implementation

Core Benchmarking Framework

Throughput Measurement System

Latency Analysis System

Resource Usage Monitor

Adaptive Performance Optimizer

MCP Integration Hooks

Performance Metrics Storage

Neural Performance Learning

리뷰

댓글

같은 저장소의 스킬

add-model-descriptions

adr-create

adr-index

adr-reindex

adr-review

adr-verify