Run comprehensive worker system benchmarks and performance analysis
View on GitHubFebruary 3, 2026
Select agents to install to:
npx add-skill https://github.com/majiayu000/claude-skill-registry/blob/fa5c745190a41547f86f6937d19a78edcddb0061/skills/worker-benchmarks/SKILL.md -a claude-code --skill worker-benchmarksInstallation paths:
.claude/skills/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 ─────────────