本文へ移動
cccskills
無料GitHub で公開

worker-benchmarks

Run comprehensive worker system benchmarks and performance analysis

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/ruflo7.4万2026年10月10日 更新

Create a new Architecture Decision Record with sequential numbering and AgentDB registration

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/ruflo7.4万2026年10月10日 更新

adr-index

無料

Build or rebuild the ADR index + dependency graph by running scripts/import.mjs (handles v3-style and plugin-style ADR formats; one Bash call vs hundreds of MCP round-trips)

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/ruflo7.4万2026年10月10日 更新

Reconcile the ADR index against a DELETED ADR file or relation line by dropping and rebuilding adr-patterns + adr-edges from scratch (scripts/reindex.mjs). Use when adr-index alone leaves stale rows behind.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/ruflo7.4万2026年10月10日 更新

Review code changes against accepted ADRs for compliance violations

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/ruflo7.4万2026年10月10日 更新

Read back adr-patterns + adr-edges namespaces, surface dangling refs / supersede cycles / status mismatches; exit 1 on cycles

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/ruflo7.4万2026年10月10日 更新

ruvnet のスキルをすべて見る

このスキルの問題を報告する