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ZekiogZekiog/ruflo/.agents/skills/worker-benchmarks/skill.md

worker-benchmarks

Skill2 starsChanged 7 months agoMITView on GitHub

Run comprehensive worker system benchmarks and performance analysis

We split this skill into the rules it gives an agent

1 rule. 1 is common, 0 are distinctive, and 0 are very specific to this project.

  • 1

    Common

    Most of the 6 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Run comprehensive performance benchmarks for the agentic-flow worker system.

    line 15

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
skill.md

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

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Trending

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Aug 30: 0 new projectsWeek of Sep 6: 0 new projectsWeek of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projects
Aug 2026Sep 2026Oct 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

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