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Worker Benchmarks

  • 995 installs
  • 67k repo stars
  • Updated August 4, 2026
  • ruvnet/ruflo

worker-benchmarks is an agentic-flow skill at version 1.0.0 that runs comprehensive worker system benchmarks and performance analysis for developers who must optimize background workers before scaling.

About

worker-benchmarks is a version 1.0.0 invocable skill from ruvnet/ruflo for the agentic-flow worker system. It runs npx agentic-flow workers benchmark for a full suite or targets four benchmark types: trigger-detection, registry, agent-selection, and concurrent. Capabilities include performance_testing, metrics_collection, and optimization_recommendations so engineers can profile worker bottlenecks before scaling task dispatch. Developers reach for worker-benchmarks when agentic-flow workers need quantified baselines instead of guessing registry or selection latency under load.

  • Runs 5 specialized benchmark suites including trigger-detection, registry, agent-selection, model cache, and concurrent
  • Delivers p95 latency targets, throughput, histograms, hit rates, and per-operation breakdowns
  • Provides optimization recommendations based on real performance data
  • Supports both full benchmark suite and targeted single-type runs via CLI flags

Worker Benchmarks by the numbers

  • 995 all-time installs (skills.sh)
  • +5 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #1,092 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/ruvnet/ruflo --skill worker-benchmarks

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Listed on Skillselion
Installs995
repo stars67k
Security audit3 / 3 scanners passed
Last updatedAugust 4, 2026
Repositoryruvnet/ruflo

How do you benchmark agentic-flow worker performance?

Measure and optimize the performance of their agentic worker system before scaling.

Who is it for?

Developers running agentic-flow background workers who need typed benchmarks before scaling concurrent dispatch.

Skip if: Projects not using agentic-flow workers or teams needing only LLM token-cost profiling.

When should I use this skill?

A developer asks to benchmark agentic-flow workers, measure trigger-detection or agent-selection latency, or optimize worker registry performance.

What you get

Worker benchmark metrics, performance analysis output, and optimization recommendations for agentic-flow background workers.

  • Worker benchmark metrics report
  • Optimization recommendations

By the numbers

  • Version 1.0.0 invocable skill with 3 declared capabilities: performance_testing, metrics_collection, optimization_recomm
  • Supports 4 benchmark types: trigger-detection, registry, agent-selection, and concurrent

Files

SKILL.mdMarkdownGitHub ↗

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

Related skills

How it compares

Choose worker-benchmarks for agentic-flow worker CLI benchmarks rather than generic HTTP load generators.

FAQ

What benchmark types does worker-benchmarks support?

worker-benchmarks supports four agentic-flow worker benchmark types—trigger-detection, registry, agent-selection, and concurrent—via npx agentic-flow workers benchmark --type flags or a full suite run without --type.

What CLI command starts worker-benchmarks?

worker-benchmarks starts with npx agentic-flow workers benchmark for the full suite, or npx agentic-flow workers benchmark --type with trigger-detection, registry, agent-selection, or concurrent for targeted runs.

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.

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