Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
5,427,056 AI resources indexedWorking toward the world’s largest AI resource index.
Run comprehensive worker system benchmarks and performance analysis
---
name: worker-benchmarks-f1a8d511da98
description: Run comprehensive worker system benchmarks and performance analysis
version: 1.0.0
invocable: true
author: agentic-flow
capabilities:
- performance_testing
- metrics_collection
- optimization_recommendations
---
# Worker Benchmarks Skill
Run comprehensive performance benchmarks for the agentic-flow worker system.
## Quick Start
```bash
# 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$settings.json`:
```json
{
"performance": {
"benchmarkThresholds": {
"triggerDetection": { "p95Ms": 5 },
"workerRegistry": { "p95Ms": 10 },
"agentSelection": { "p95Ms": 1 },
"memoryKeyGeneration": { "p95Ms": 0.1 },
"concurrentWorkers": { "totalMs": 1000 }
}
}
}
```
## Programmatic Usage
```typescript
import { workerBenchmarks, runBenchmarks } from 'agentic-flow$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
---
name: worker-benchmarks-f1a8d511da98
description: Run comprehensive worker system benchmarks and performance analysis
version: 1.0.0
invocable: true
author: agentic-flow
capabilities:
- performance_testing
- metrics_collection
- optimization_recommendations
---
# Worker Benchmarks Skill
Run comprehensive performance benchmarks for the agentic-flow worker system.
## Quick Start
```bash
# 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`:
```json
{
"performance": {
"benchmarkThresholds": {
"triggerDetection": { "p95Ms": 5 },
"workerRegistry": { "p95Ms": 10 },
"agentSelection": { "p95Ms": 1 },
"memoryKeyGeneration": { "p95Ms": 0.1 },
"concurrentWorkers": { "totalMs": 1000 }
}
}
}
```
## Programmatic Usage
```typescript
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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.