
Agent Performance Benchmarker
Run structured throughput, latency, and resource benchmarks on distributed consensus-style workloads before you ship or tune agent orchestration.
Overview
Agent Performance Benchmarker is an agent skill most often used in Ship (also Build integrations, Operate monitoring) that measures throughput, latency, and resource use for distributed consensus protocols and returns co
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-performance-benchmarkerWhat is this skill?
- Measures throughput, latency, and scalability across consensus-style protocol comparisons
- Tracks CPU, memory, network, and storage utilization during benchmark runs
- Compares Byzantine, Raft, and Gossip protocol performance in one workflow
- Supports adaptive tuning and load-balancing guidance from collected metrics
- Emits post-run performance reports with optimization recommendations
- 5 core responsibility areas including adaptive tuning and reporting
Adoption & trust: 637 installs on skills.sh; 58.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You are about to ship or scale a distributed coordination backend but lack repeatable throughput, latency, and resource benchmarks across protocol options.
Who is it for?
Indie builders benchmarking agent swarms, consensus prototypes, or backend coordination layers before production load.
Skip if: Teams that only need frontend UI patterns or quick smoke tests without distributed protocol context.
When should I use this skill?
Invoke when the task involves benchmarking distributed consensus or performance analysis for coordination protocols.
What do I get? / Deliverables
After a run you get a compiled benchmarking report with comparative metrics and optimization recommendations you can use to tune parameters or justify a protocol choice.
- Performance analysis report
- Comparative protocol metrics
- Optimization recommendations
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Performance benchmarking and optimization reports belong on the shipping shelf where solo builders validate that systems meet latency and throughput targets under load. Perf is the canonical home for comparative protocol analysis, adaptive tuning recommendations, and actionable metric collection—not generic unit tests.
Where it fits
Compare Raft versus Gossip implementations while wiring a new coordination service for your agent product.
Collect throughput and latency baselines before tagging a release as production-ready.
Re-benchmark after a traffic spike to see if adaptive tuning suggestions still hold.
How it compares
Structured benchmark analyst for consensus-style backends, not a generic linter or single-endpoint load script.
Common Questions / FAQ
Who is agent-performance-benchmarker for?
Solo and small-team builders running distributed or multi-agent backends who need measured throughput, latency, and resource profiles before tuning or shipping.
When should I use agent-performance-benchmarker?
Use it in Ship perf when validating scale targets, in Build integrations when comparing coordination protocols, and in Operate monitoring when revisiting production performance after architecture changes.
Is agent-performance-benchmarker safe to install?
Review the Security Audits panel on this Prism page and inspect the skill’s shell hooks and monitoring side effects in your repo before running benchmarks against production systems.
SKILL.md
READMESKILL.md - Agent Performance Benchmarker
--- 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),