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/worker-benchmarks

Run comprehensive worker system benchmarks and performance analysis

From plugin
claude-flow
67k200 skills157 agents194 commands1 MCP
Install
$ npx -y skills add ruvnet/claude-flow --skill worker-benchmarks --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/worker-benchmarks

Context preview

The summary Claude sees to decide when to auto-load this skill.

Run comprehensive worker system benchmarks and performance analysis

SKILL.md

worker-benchmarks.SKILL.md
name: worker-benchmarks
description: Run comprehensive worker system benchmarks and performance analysis
user-invocable: true

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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Ships withclaude-flow

An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.

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