/worker-benchmarks
Run comprehensive worker system benchmarks and performance analysis
$ npx -y skills add ruvnet/ruflo --skill worker-benchmarks --agent claude-codeHow 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.mdname: 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
Read more
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
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.
Repo: ruvnet/ruflo
Other skills on claude-flow.
- /agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Open skill - /agentdb-learning
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Open skill - /agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Open skill - /agentdb-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Open skill - /agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Open skill - /agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Open skill

