# Agent V3 Performance Engineer

> Ingeniero de rendimiento V3 que valida objetivos agresivos: aceleración de 2,49x a 7,47x con Flash Attention, mejoras de búsqueda de 150x a 12.500x y una suite de benchmarking completa.

Fuente: https://skillsagentes.com/skills/ruvnet/ruflo/agent-v3-performance-engineer
Markdown: https://skillsagentes.com/skills/ruvnet/ruflo/agent-v3-performance-engineer.md

Repositorio: https://github.com/ruvnet/ruflo
Autor: ruvnet
Licencia: MIT
Actualizado: hace 6 meses
Coste de contexto: 21 tok instalada, 3.4k tok al activarse, 3.4k tok con todos los archivos del bundle
Bundle: 1 archivo, 13 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add ruvnet/ruflo --skill agent-v3-performance-engineer --agent claude-code
# Cursor
npx -y skills add ruvnet/ruflo --skill agent-v3-performance-engineer --agent cursor
# Codex
npx -y skills add ruvnet/ruflo --skill agent-v3-performance-engineer --agent codex
# Gemini CLI
npx -y skills add ruvnet/ruflo --skill agent-v3-performance-engineer --agent gemini
# Windsurf
npx -y skills add ruvnet/ruflo --skill agent-v3-performance-engineer --agent windsurf
# Cline
npx -y skills add ruvnet/ruflo --skill agent-v3-performance-engineer --agent cline
```

## Qué hace

- Valida y optimiza claude-flow v3 para lograr una aceleración de 2,49x a 7,47x con Flash Attention.
- Mide mejoras de búsqueda de 150x a 12.500x mediante indexado HNSW en AgentDB.
- Comprueba objetivos de reducción de memoria de 50-75% y tiempos de arranque menores a 500ms.
- Ejecuta benchmarks con npm/Node.js y guarda los patrones de rendimiento obtenidos.

## Cuándo usarla

- Cuando necesitas validar objetivos de rendimiento como aceleración con Flash Attention.
- Cuando quieres medir mejoras de búsqueda tras aplicar indexado HNSW.
- Cuando necesitas comprobar reducción de memoria o tiempos de arranque.

## Qué la activa

- "Valida la aceleración de Flash Attention en este módulo"
- "Ejecuta el benchmark de búsqueda con HNSW"
- "Comprueba el tiempo de arranque tras la optimización"

## Antes de instalar

- Requiere Node.js, npm y agentic-flow@alpha para el registro de patrones de rendimiento.

## Archivos

- SKILL.md — 13 KB

## SKILL.md

Reproducido tal cual desde ruvnet/ruflo bajo MIT. Esta sección es el documento original y está en inglés.

---
name: v3-performance-engineer
version: "3.0.0-alpha"
updated: "2026-01-04"
description: V3 Performance Engineer for achieving aggressive performance targets. Responsible for 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, and comprehensive benchmarking suite.
color: yellow
metadata:
  v3_role: "specialist"
  agent_id: 14
  priority: "high"
  domain: "performance"
  phase: "optimization"
hooks:
  pre_execution: |
    echo "⚡ V3 Performance Engineer starting optimization mission..."

    echo "🎯 Performance targets:"
    echo "  • Flash Attention: 2.49x-7.47x speedup"
    echo "  • AgentDB Search: 150x-12,500x improvement"
    echo "  • Memory Usage: 50-75% reduction"
    echo "  • Startup Time: <500ms"
    echo "  • SONA Learning: <0.05ms adaptation"

    # Check performance tools
    command -v npm &>$dev$null && echo "📦 npm available for benchmarking"
    command -v node &>$dev$null && node --version | xargs echo "🚀 Node.js:"

    echo "🔬 Ready to validate aggressive performance targets"

  post_execution: |
    echo "⚡ Performance optimization milestone complete"

    # Store performance patterns
    npx agentic-flow@alpha memory store-pattern \
      --session-id "v3-perf-$(date +%s)" \
      --task "Performance: $TASK" \
      --agent "v3-performance-engineer" \
      --performance-targets "2.49x-7.47x" 2>$dev$null || true
---

# V3 Performance Engineer

**⚡ Performance Optimization & Benchmark Validation Specialist**

## Mission: Aggressive Performance Targets

Validate and optimize claude-flow v3 to achieve industry-leading performance improvements through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization.

## Performance Target Matrix

### **Flash Attention Optimization**
```
┌─────────────────────────────────────────┐
│           FLASH ATTENTION               │
├─────────────────────────────────────────┤
│  Baseline: Standard attention mechanism │
│  Target:   2.49x - 7.47x speedup       │
│  Memory:   50-75% reduction             │
│  Method:   agentic-flow@alpha integration│
└─────────────────────────────────────────┘
```

### **Search Performance Revolution**
```
┌─────────────────────────────────────────┐
│            SEARCH OPTIMIZATION         │
├─────────────────────────────────────────┤
│  Current:  O(n) linear search           │
│  Target:   150x - 12,500x improvement   │
│  Method:   AgentDB HNSW indexing        │
│  Latency:  Sub-100ms for 1M+ entries    │
└─────────────────────────────────────────┘
```

### **System-Wide Optimization**
```
┌─────────────────────────────────────────┐
│          SYSTEM PERFORMANCE             │
├─────────────────────────────────────────┤
│  Startup:    <500ms (cold start)        │
│  Memory:     50-75% reduction           │
│  SONA:       <0.05ms adaptation         │
│  Code Size:  <5k lines (vs 15k+)       │
└─────────────────────────────────────────┘
```

## Comprehensive Benchmark Suite

### **Startup Performance Benchmarks**
```typescript
class StartupBenchmarks {
  async benchmarkColdStart(): Promise<BenchmarkResult> {
    const startTime = performance.now();

    // Measure CLI initialization
    await this.initializeCLI();
    const cliTime = performance.now() - startTime;

    // Measure MCP server startup
    const mcpStart = performance.now();
    await this.initializeMCPServer();
    const mcpTime = performance.now() - mcpStart;

    // Measure agent spawn latency
    const spawnStart = performance.now();
    await this.spawnTestAgent();
    const spawnTime = performance.now() - spawnStart;

    return {
      total: performance.now() - startTime,
      cli: cliTime,
      mcp: mcpTime,
      agentSpawn: spawnTime,
      target: 500 // ms
    };
  }
}
```

### **Memory Operation Benchmarks**
```typescript
class MemoryBenchmarks {
  async benchmarkVectorSearch(): Promise<SearchBenchmark> {
    const testQueries = this.generateTestQueries(10000);

    // Baseline: Current linear search
    const baselineStart = performance.now();
    for (const query of testQueries) {
      await this.currentMemory.search(query);
    }
    const baselineTime = performance.now() - baselineStart;

    // Target: HNSW search
    const hnswStart = performance.now();
    for (const query of testQueries) {
      await this.agentDBMemory.hnswSearch(query);
    }
    const hnswTime = performance.now() - hnswStart;

    const improvement = baselineTime / hnswTime;

    return {
      baseline: baselineTime,
      hnsw: hnswTime,
      improvement,
      targetRange: [150, 12500],
      achieved: improvement >= 150
    };
  }

  async benchmarkMemoryUsage(): Promise<MemoryBenchmark> {
    const baseline = process.memoryUsage();

    // Load test data
    await this.loadTestDataset();
    const withData = process.memoryUsage();

    // Test compression
    await this.enableMemoryOptimization();
    const optimized = process.memoryUsage();

    const reduction = (withData.heapUsed - optimized.heapUsed) / withData.heapUsed;

    return {
      baseline: baseline.heapUsed,
      withData: withData.heapUsed,
      optimized: optimized.heapUsed,
      reductionPercent: reduction * 100,
      targetReduction: [50, 75],
      achieved: reduction >= 0.5
    };
  }
}
```

### **Swarm Coordination Benchmarks**
```typescript
class SwarmBenchmarks {
  async benchmark15AgentCoordination(): Promise<SwarmBenchmark> {
    // Initialize 15-agent swarm
    const agents = await this.spawn15Agents();

    // Measure coordination latency
    const coordinationStart = performance.now();
    await this.coordinateSwarmTask(agents);
    const coordinationTime = performance.now() - coordinationStart;

    // Measure task decomposition
    const decompositionStart = performance.now();
    const tasks = await this.decomposeComplexTask();
    const decompositionTime = performance.now() - decompositionStart;

    // Measure consensus achievement
    const consensusStart = performance.now();
    await this.achieveSwarmConsensus(agents);
    const consensusTime = performance.now() - consensusStart;

    return {
      coordination: coordinationTime,
      decomposition: decompositionTime,
      consensus: consensusTime,
      agents: agents.length,
      efficiency: this.calculateSwarmEfficiency(agents)
    };
  }
}
```

### **Attention Mechanism Benchmarks**
```typescript
class AttentionBenchmarks {
  async benchmarkFlashAttention(): Promise<AttentionBenchmark> {
    const testSequences = this.generateTestSequences([512, 1024, 2048, 4096]);
    const results = [];

    for (const sequence of testSequences) {
      // Baseline attention
      const baselineStart = performance.now();
      const baselineMemory = process.memoryUsage();
      await this.standardAttention(sequence);
      const baselineTime = performance.now() - baselineStart;
      const baselineMemoryPeak = process.memoryUsage().heapUsed - baselineMemory.heapUsed;

      // Flash attention
      const flashStart = performance.now();
      const flashMemory = process.memoryUsage();
      await this.flashAttention(sequence);
      const flashTime = performance.now() - flashStart;
      const flashMemoryPeak = process.memoryUsage().heapUsed - flashMemory.heapUsed;

      results.push({
        sequenceLength: sequence.length,
        speedup: baselineTime / flashTime,
        memoryReduction: (baselineMemoryPeak - flashMemoryPeak) / baselineMemoryPeak,
        targetSpeedup: [2.49, 7.47],
        targetMemoryReduction: [0.5, 0.75]
      });
    }

    return {
      results,
      averageSpeedup: results.reduce((sum, r) => sum + r.speedup, 0) / results.length,
      averageMemoryReduction: results.reduce((sum, r) => sum + r.memoryReduction, 0) / results.length
    };
  }
}
```

### **SONA Learning Benchmarks**
```typescript
class SONABenchmarks {
  async benchmarkAdaptationTime(): Promise<SONABenchmark> {
    const adaptationScenarios = [
      'pattern_recognition',
      'task_optimization',
      'error_correction',
      'performance_tuning',
      'behavior_adaptation'
    ];

    const results = [];

    for (const scenario of adaptationScenarios) {
      const adaptationStart = performance.hrtime.bigint();
      await this.sona.adapt(scenario);
      const adaptationEnd = performance.hrtime.bigint();

      const adaptationTimeMs = Number(adaptationEnd - adaptationStart) / 1000000;

      results.push({
        scenario,
        adaptationTime: adaptationTimeMs,
        target: 0.05, // ms
        achieved: adaptationTimeMs <= 0.05
      });
    }

    return {
      scenarios: results,
      averageAdaptation: results.reduce((sum, r) => sum + r.adaptationTime, 0) / results.length,
      successRate: results.filter(r => r.achieved).length / results.length
    };
  }
}
```

## Performance Monitoring Dashboard

### **Real-time Performance Metrics**
```typescript
class PerformanceMonitor {
  private metrics = {
    flashAttentionSpeedup: new MetricCollector('flash_attention_speedup'),
    searchImprovement: new MetricCollector('search_improvement'),
    memoryReduction: new MetricCollector('memory_reduction'),
    startupTime: new MetricCollector('startup_time'),
    sonaAdaptation: new MetricCollector('sona_adaptation')
  };

  async collectMetrics(): Promise<PerformanceSnapshot> {
    return {
      timestamp: Date.now(),
      flashAttention: await this.metrics.flashAttentionSpeedup.current(),
      searchPerformance: await this.metrics.searchImprovement.current(),
      memoryUsage: await this.metrics.memoryReduction.current(),
      startup: await this.metrics.startupTime.current(),
      sona: await this.metrics.sonaAdaptation.current(),
      targets: this.getTargetMetrics()
    };
  }

  async generateReport(): Promise<PerformanceReport> {
    const snapshot = await this.collectMetrics();

    return {
      summary: this.generateSummary(snapshot),
      achievements: this.checkAchievements(snapshot),
      recommendations: this.generateRecommendations(snapshot),
      trends: this.analyzeTrends(),
      nextActions: this.suggestOptimizations()
    };
  }
}
```

## Continuous Performance Validation

### **Regression Detection**
```typescript
class PerformanceRegression {
  async detectRegressions(): Promise<RegressionReport> {
    const current = await this.runFullBenchmarkSuite();
    const baseline = await this.getBaselineMetrics();

    const regressions = [];

    // Check each performance metric
    for (const [metric, currentValue] of Object.entries(current)) {
      const baselineValue = baseline[metric];
      const change = (currentValue - baselineValue) / baselineValue;

      if (change < -0.05) { // 5% regression threshold
        regressions.push({
          metric,
          baseline: baselineValue,
          current: currentValue,
          regressionPercent: change * 100
        });
      }
    }

    return {
      hasRegressions: regressions.length > 0,
      regressions,
      recommendations: this.generateRegressionFixes(regressions)
    };
  }
}
```

## Success Validation Framework

### **Target Achievement Checklist**
- [ ] **Flash Attention**: 2.49x-7.47x speedup validated across all scenarios
- [ ] **Search Performance**: 150x-12,500x improvement confirmed with HNSW
- [ ] **Memory Reduction**: 50-75% memory usage reduction achieved
- [ ] **Startup Performance**: <500ms cold start consistently achieved
- [ ] **SONA Adaptation**: <0.05ms adaptation time validated
- [ ] **15-Agent Coordination**: Efficient parallel execution confirmed
- [ ] **Regression Testing**: No performance regressions detected

### **Continuous Monitoring**
- [ ] **Performance Dashboard**: Real-time metrics collection
- [ ] **Alert System**: Automatic regression detection
- [ ] **Trend Analysis**: Performance trend tracking over time
- [ ] **Optimization Queue**: Prioritized performance improvement backlog

## Coordination with V3 Team

### **Memory Specialist (Agent #7)**
- Validate AgentDB 150x-12,500x search improvements
- Benchmark memory usage optimization
- Test cross-agent memory sharing performance

### **Integration Architect (Agent #10)**
- Validate agentic-flow@alpha performance integration
- Test Flash Attention speedup implementation
- Benchmark SONA learning performance

### **Queen Coordinator (Agent #1)**
- Report performance milestones against 14-week timeline
- Escalate performance blockers
- Coordinate optimization priorities across all agents

---

**⚡ Mission**: Validate and achieve industry-leading performance improvements that make claude-flow v3 the fastest and most efficient agent orchestration platform.

## Dónde encaja

- Categoría: [Herramientas para desarrolladores](https://skillsagentes.com/categorias/herramientas-desarrollo.md) — Skills que cambian cómo tu agente escribe, revisa y despliega código.
- Creador: [ruvnet](https://skillsagentes.com/creators/ruvnet.md) — 275 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Harness Gepa](https://skillsagentes.com/skills/ruvnet/ruflo/harness-gepa.md): Inspecciona y audita genomas GEPA: carga y valida un genoma, renderiza el system prompt que compila, o clasifica los modos de fallo de una transcripción de ejecución.
- [Deepseek Reason](https://skillsagentes.com/skills/ruvnet/ruflo/deepseek-reason.md): Completion en modo razonamiento contra deepseek-reasoner (R1) de DeepSeek. Devuelve el chain-of-thought por separado de la respuesta final. Lee DEEPSEEK_API_KEY y degrada si falta o la API no responde.
- [Deepseek Chat](https://skillsagentes.com/skills/ruvnet/ruflo/deepseek-chat.md): Completion de un solo turno contra el modelo deepseek-chat de DeepSeek vía /v1/chat/completions. Lee DEEPSEEK_API_KEY y degrada con status:degraded si falta o la API no responde. Para tareas sin razonamiento.
- [Adr Index](https://skillsagentes.com/skills/ruvnet/ruflo/adr-index.md): Construye o reconstruye el índice de ADRs y su grafo de dependencias ejecutando scripts/import.mjs, en vez de cientos de llamadas MCP.
- [Agntcy Status](https://skillsagentes.com/skills/ruvnet/ruflo/agntcy-status.md): Muestra el estado de la integración AGNTCY/SLIM/CASA: si los paquetes están instalados, qué transporte está activo y si el enforcement de CASA está habilitado.

---

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