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chore: restore .claude commands, agents, helpers & skills (lost in 1df208d)

Browse Source 
Restores the entire .claude/ infrastructure that was accidentally deleted
in commit 1df208d ('feat(timeline): add pulse animation for in-flight drag
mutations'). Recovered via git checkout 1df208d^.

Restored:
- .claude/commands/ (gitlooper, sparc/, github/, automation/, monitoring/,
  optimization/, hooks/, plan, implement, research, review, perf, visualaudit)
- .claude/agents/ (core/, github/, sparc/, v3/, swarm/, templates/, ...)
- .claude/helpers/ (41 scripts incl. hook-handler.cjs, statusline.cjs)
- .claude/skills/ (20 skills incl. sparc-methodology, github-*, v3-*)
- .claude/settings.json (hooks configuration)

Also updated all CapaKraken → Nexus references in affected command files.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Hartmut
2026-08-15 11:43:52 +02:00
co-authored by Claude Sonnet 4.6
parent 55b861cb43
commit f80808482d
269 changed files with 80697 additions and 0 deletions
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---
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
1. **Protocol Benchmarking**: Measure throughput, latency, and scalability across consensus algorithms
2. **Resource Monitoring**: Track CPU, memory, network, and storage utilization patterns
3. **Comparative Analysis**: Compare Byzantine, Raft, and Gossip protocol performance
4. **Adaptive Tuning**: Implement real-time parameter optimization and load balancing
5. **Performance Reporting**: Generate actionable insights and optimization recommendations
## Technical Implementation
### Core Benchmarking Framework
```javascript
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
```javascript
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
```javascript
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
```javascript
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
```javascript
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
```javascript
// 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
```javascript
// 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.