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worker-benchmarks

Run comprehensive worker system benchmarks and performance analysis

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Quellinformationen

Repository
ruvnet/ruflo
Letzte Quellaktivität
19. Mai 2026 um 14:14
Erkannte Sprache von SKILL.md
Englisch
Sterne
73.010
Forks
8.665

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
worker-benchmarks
description
Run comprehensive worker system benchmarks and performance analysis
version
1.0.0
invocable
true
author
agentic-flow
capabilities
["performance_testing","metrics_collection","optimization_recommendations"]
# Worker Benchmarks Skill Run comprehensive performance benchmarks for the agentic-flow worker system. ## Quick Start ```bash # 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$settings.json`: ```json { "performance": { "benchmarkThresholds": { "triggerDetection": { "p95Ms": 5 }, "workerRegistry": { "p95Ms": 10 }, "agentSelection": { "p95Ms": 1 }, "memoryKeyGeneration": { "p95Ms": 0.1 }, "concurrentWorkers": { "totalMs": 1000 } } } } ``` ## Programmatic Usage ```typescript import { workerBenchmarks, runBenchmarks } from 'agentic-flow$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 1. **Model Cache**: Enable with `CLAUDE_FLOW_MODEL_CACHE_MB=512` 2. **Parallel Workers**: Enable with `CLAUDE_FLOW_WORKER_PARALLEL=true` 3. **Warning Suppression**: Enable with `CLAUDE_FLOW_SUPPRESS_WARNINGS=true` 4. **SQLite WAL Mode**: Automatic for better concurrent performance
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