System-level performance benchmarks for workers.
Install
mkdir -p .claude/skills/worker-benchmarks && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1052" && unzip -o skill.zip -d .claude/skills/worker-benchmarks && rm skill.zipInstalls to .claude/skills/worker-benchmarks
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Run comprehensive worker system benchmarks and performance analysisKey capabilities
- →Measure latency for trigger detection
- →Evaluate CRUD operation speed in worker registries
- →Test parallel worker creation performance
- →Analyze model cache hit rates
- →Generate memory key patterns
How it works
The tool executes specific performance tests against worker system components and compares results against p95 latency thresholds defined in settings. It provides both CLI-based reporting and programmatic access to benchmark results.
Inputs & outputs
When to use worker-benchmarks
- →Benchmark trigger speed
- →Analyze registry latency
- →Test concurrent worker performance
About this skill
Worker Benchmarks Skill
Run comprehensive performance benchmarks for the agentic-flow worker system.
Quick Start
# Run full benchmark suite
npx agentic-flow workers benchmark
# Run specific benchmark
npx agentic-flow workers benchmark --type trigger-detection
npx agentic-flow workers benchmark --type registry
npx agentic-flow workers benchmark --type agent-selection
npx agentic-flow workers benchmark --type concurrent
Benchmark Types
1. Trigger Detection (trigger-detection)
Tests keyword detection speed across 12 worker triggers.
- Target: p95 < 5ms
- Iterations: 1000
- Metrics: latency, throughput, histogram
2. Worker Registry (registry)
Tests CRUD operations on worker entries.
- Target: p95 < 10ms
- Iterations: 500 creates, gets, updates
- Metrics: per-operation latency breakdown
3. Agent Selection (agent-selection)
Tests performance-based agent selection.
- Target: p95 < 1ms
- Iterations: 1000
- Metrics: selection confidence, agent scores
4. Model Cache (cache)
Tests model caching performance.
- Target: p95 < 0.5ms
- Metrics: hit rate, cache size, eviction stats
5. Concurrent Workers (concurrent)
Tests parallel worker creation and updates.
- Target: < 1000ms for 10 workers
- Metrics: per-worker latency, memory usage
6. Memory Key Generation (memory-keys)
Tests memory pattern key generation.
- Target: p95 < 0.1ms
- Iterations: 5000
- Metrics: unique patterns, throughput
Output Format
═══════════════════════════════════════════════════════════
📈 BENCHMARK RESULTS
═══════════════════════════════════════════════════════════
✅ Trigger Detection
Operation: detect
Count: 1,000
Avg: 0.045ms | p95: 0.120ms (target: 5ms)
Throughput: 22,222 ops$s
Memory Δ: 0.12MB
✅ Worker Registry
Operation: crud
Count: 1,500
Avg: 1.234ms | p95: 3.456ms (target: 10ms)
Throughput: 810 ops$s
Memory Δ: 2.34MB
───────────────────────────────────────────────────────────
📊 SUMMARY
───────────────────────────────────────────────────────────
Total Tests: 6
Passed: 6 | Failed: 0
Avg Latency: 0.567ms
Total Duration: 2345ms
Peak Memory: 8.90MB
═══════════════════════════════════════════════════════════
Integration with Settings
Benchmark thresholds are configured in .claude$settings.json:
{
"performance": {
"benchmarkThresholds": {
"triggerDetection": { "p95Ms": 5 },
"workerRegistry": { "p95Ms": 10 },
"agentSelection": { "p95Ms": 1 },
"memoryKeyGeneration": { "p95Ms": 0.1 },
"concurrentWorkers": { "totalMs": 1000 }
}
}
}
Programmatic Usage
import { workerBenchmarks, runBenchmarks } from 'agentic-flow$workers$worker-benchmarks';
// Run full suite
const suite = await runBenchmarks();
console.log(suite.summary);
// Run individual benchmarks
const triggerResult = await workerBenchmarks.benchmarkTriggerDetection(1000);
const registryResult = await workerBenchmarks.benchmarkRegistryOperations(500);
Performance Optimization Tips
- Model Cache: Enable with
CLAUDE_FLOW_MODEL_CACHE_MB=512 - Parallel Workers: Enable with
CLAUDE_FLOW_WORKER_PARALLEL=true - Warning Suppression: Enable with
CLAUDE_FLOW_SUPPRESS_WARNINGS=true - SQLite WAL Mode: Automatic for better concurrent performance
When not to use it
- →Non-worker based system architectures
Prerequisites
Limitations
- →Thresholds are strictly tied to settings in .claude/settings.json
- →Results are specific to the agentic-flow worker system
How it compares
It provides standardized performance metrics for specific worker operations instead of requiring custom-built timing scripts.
Compared to similar skills
worker-benchmarks side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| worker-benchmarks (this skill) | 3 | 2mo | Review | Advanced |
| sentry-rate-limits | 1 | 25d | Caution | Intermediate |
| optimizing-performance | 1 | 2mo | Review | Intermediate |
| azure-monitor-opentelemetry-ts | 1 | 3mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by ruvnet
View all by ruvnet →You might also like
sentry-rate-limits
jeremylongshore
Manage Sentry rate limits and quota optimization. Use when hitting rate limits, optimizing event volume, or managing Sentry costs. Trigger with phrases like "sentry rate limit", "sentry quota", "reduce sentry events", "sentry 429".
optimizing-performance
CloudAI-X
Analyzes and optimizes application performance across frontend, backend, and database layers. Use when diagnosing slowness, improving load times, optimizing queries, reducing bundle size, or when asked about performance issues.
azure-monitor-opentelemetry-ts
microsoft
Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.
sentry-performance-tracing
jeremylongshore
Execute set up performance monitoring and distributed tracing with Sentry. Use when implementing performance tracking, tracing requests, or monitoring application performance. Trigger with phrases like "sentry performance", "sentry tracing", "sentry APM", "monitor performance sentry".
sentry-architecture-variants
jeremylongshore
Execute sentry architecture patterns for different application types. Use when setting up Sentry for monoliths, microservices, serverless, or hybrid architectures. Trigger with phrases like "sentry monolith setup", "sentry microservices", "sentry serverless", "sentry architecture pattern".
langfuse-rate-limits
jeremylongshore
Implement Langfuse rate limiting, batching, and backoff patterns. Use when handling rate limit errors, optimizing trace ingestion, or managing high-volume LLM observability workloads. Trigger with phrases like "langfuse rate limit", "langfuse throttling", "langfuse 429", "langfuse batching", "langfuse high volume".