Runs performance benchmarks for agentic-flow worker systems, including trigger detection, registry CRUD, agent selection, model cache, concurrent workers, and memory key generation. Use when diagnosing worker performance or comparing configurations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/claude-skills-library:worker-benchmarksThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Run comprehensive performance benchmarks for the agentic-flow worker system.
Run comprehensive performance benchmarks for the agentic-flow worker system.
# 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
trigger-detection)Tests keyword detection speed across 12 worker triggers.
registry)Tests CRUD operations on worker entries.
agent-selection)Tests performance-based agent selection.
cache)Tests model caching performance.
concurrent)Tests parallel worker creation and updates.
memory-keys)Tests memory pattern key generation.
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📈 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
═══════════════════════════════════════════════════════════
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 }
}
}
}
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);
CLAUDE_FLOW_MODEL_CACHE_MB=512CLAUDE_FLOW_WORKER_PARALLEL=trueCLAUDE_FLOW_SUPPRESS_WARNINGS=truenpx claudepluginhub frankxai/claude-skills-library --plugin claude-skills-libraryCoordinates workers and specialized agents for task dispatch, routing, and performance tracking. Supports self-learning agent selection via execution history and feedback loops.
Orchestrates online benchmarks for vLLM inference services using `vllm bench serve`. Supports single/multi-case batch execution with result aggregation and auto-optimization for throughput under latency SLOs (TTFT, TPOT, P99).
Validates, runs, and debugs multi-agent YAML workflows. Use when orchestrating AI agents, configuring routing, or setting up human-in-the-loop gates.