WO

worker-benchmarks

System-level performance benchmarks for workers.

Install

mkdir -p .claude/skills/worker-benchmarks && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1052" && unzip -o skill.zip -d .claude/skills/worker-benchmarks && rm skill.zip

Installs to .claude/skills/worker-benchmarks

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Run comprehensive worker system benchmarks and performance analysis
67 charsno explicit “when” trigger
Advanced

Key capabilities

  • Measure latency for trigger detection
  • Evaluate CRUD operation speed in worker registries
  • Test parallel worker creation performance
  • Analyze model cache hit rates
  • Generate memory key patterns

How it works

The tool executes specific performance tests against worker system components and compares results against p95 latency thresholds defined in settings. It provides both CLI-based reporting and programmatic access to benchmark results.

Inputs & outputs

You give it
Benchmark type or full suite command
You get back
Latency metrics and throughput statistics

When to use worker-benchmarks

  • Benchmark trigger speed
  • Analyze registry latency
  • Test concurrent worker performance

About this skill

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 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:

{
  "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

  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

When not to use it

  • Non-worker based system architectures

Prerequisites

agentic-flow package installedNode.js environment

Limitations

  • Thresholds are strictly tied to settings in .claude/settings.json
  • Results are specific to the agentic-flow worker system

How it compares

It provides standardized performance metrics for specific worker operations instead of requiring custom-built timing scripts.

Compared to similar skills

worker-benchmarks side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
worker-benchmarks (this skill)32moReviewAdvanced
sentry-rate-limits125dCautionIntermediate
optimizing-performance12moReviewIntermediate
azure-monitor-opentelemetry-ts13moReviewIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

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