Worker Benchmarks
Measure agentic-flow worker latency, throughput, and concurrency against documented p95 targets before you scale agent workloads.
Install
npx skills add https://github.com/ruvnet/ruflo --skill worker-benchmarksWhat is this skill?
- Full suite via `npx agentic-flow workers benchmark` plus `--type` filters for trigger-detection, registry, agent-selecti
- Per-type SLOs documented (e.g. trigger-detection p95 < 5ms, registry p95 < 10ms, agent-selection p95 < 1ms, cache p95 <
- Collects latency histograms, throughput, registry CRUD breakdown, selection confidence scores, and cache hit/eviction st
- Targets the agentic-flow worker system (12 trigger keywords in trigger-detection benchmark)
- Capabilities: performance_testing, metrics_collection, optimization_recommendations
Adoption & trust: 635 installs on skills.sh; 58.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
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Journey fit
Primary fit
Performance benchmark suites belong on the Ship shelf because solo builders run them to prove agent pipelines meet latency budgets before release. Perf is the canonical subphase for scripted benchmark types (trigger detection, registry, agent selection, cache, concurrent workers) with explicit p95 targets.
Common Questions / FAQ
Is Worker Benchmarks safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.
SKILL.md
READMESKILL.md - Worker Benchmarks
# 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