Worker Benchmarks
Run comprehensive worker system benchmarks and performance analysis
Specification
- Skill ID
- ruvnet/ruflo/worker-benchmarks
- Publisher
- ruvnet
- Repository
- ruflo
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
How to use it
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
ruvnet/ruflo/worker-benchmarksInstalls these files- SKILL.md
What this skill tells the agent
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 concurrentBenchmark 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
More from ruvnet
Use this skill in a project
Open any RiverX project, search the Skills panel for ruvnet/ruflo/worker-benchmarks, and install it in one click.
