agent-performance-benchmarker
Agent skill for performance-benchmarker - invoke with $agent-performance-benchmarker
Profile →npx skills add ruvnet/ruflo --skill agent-performance-benchmarker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Belongs to an analyst skill that implements comprehensive performance benchmarking and optimization analysis for distributed consensus protocols. It provides capabilities for throughput measurement, latency analysis, resource monitoring, comparative analysis, and adaptive tuning, with the aim of generating performance reports and optimization recommendations.
How it works
- Core Benchmarking Framework: maintains benchmark suites, performance metrics, historical data, and an adaptive optimizer. It can register benchmarks for a protocol, run comprehensive benchmarks across protocols and scenarios, and produce results including benchmark results, comparative analysis, and optimization recommendations. It executes a benchmark scenario by setting up the environment, running preparation, performing phases (throughput, latency, resource usage, scalability, fault tolerance), performing post-benchmark analysis, and cleaning up.
- Throughput Measurement System: initializes a load generator, collects metrics for transactions per second and success rate, supports ramp-up behavior, and adjusts the load rate based on observed success rates. It returns analyzed throughput results including average, max, optimal, and sustainable throughput, plus variability.
- Latency Analysis System: measures latency by performing warmup, then collecting a sample set of total latencies (submission, consensus, application phases). It analyzes latency distribution, calculates percentiles (50, 75, 90, 95, 99, 99.9, 99.99), conducts phase-specific analysis (mean, p50, p95, p99, max, contribution), and identifies outliers.
- Resource Usage Monitor: starts a background monitoring loop during benchmarks, collecting CPU, memory, network I/O, disk I/O, and process metrics at 1-second intervals, then analyzes resource usage after the benchmark.
When to use it
Use when you need a structured, automated procedure to benchmark distributed consensus protocols, compare different protocols (e.g., Byzantine, Raft, Gossip), monitor resource usage during benchmarks, and obtain adaptive recommendations based on results.
What it can touch
- Tools declared: claude-code, codex. The skill interacts with a protocol interface to submit transactions, and uses internal classes like TimeSeriesDatabase, AdaptiveOptimizer, BenchmarkSuite, LoadGenerator, MetricsCollector, SystemMonitor, and various benchmark components. It references file and class names exactly as shown in the implementation, including method names such as registerBenchmarkSuite, runComprehensiveBenchmarks, executeBenchmarkScenario, measureThroughput, generateTransactionLoad, analyzeThroughputMeasurements, measureLatency, measureSingleTransactionLatency, analyzeLatencyDistribution, startContinuousMonitoring, collectResourceMeasurement, and analyzeResourceUsage.
Caveats
- The implementation relies on many internal components and assumes availability of protocol submission methods and environment setup routines. No outcomes are guaranteed; the skill returns analytical results and recommendations based on the executed benchmarks. Licensing noted as MIT in the repository metadata. The material includes code-like snippets and class structures but no external guarantees beyond stated behavior.
--- 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
- 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
What does the agent-performance-benchmarker skill do?
Agent skill for performance-benchmarker - invoke with $agent-performance-benchmarker
How do I install it?
Run `npx skills add ruvnet/ruflo --skill agent-performance-benchmarker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.