MCPInstall

agent-v3-performance-engineer

Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-performance-engineer
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads agent-v3-performance-engineer automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.


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

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

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

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

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

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

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

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.

FAQ

What does the agent-v3-performance-engineer skill do?

Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer

How do I install agent-v3-performance-engineer?

Run npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-performance-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/agent-v3-performance-engineer/ (personal) or .claude/skills/agent-v3-performance-engineer/ (project).

Which AI agents can use agent-v3-performance-engineer?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is agent-v3-performance-engineer safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains agent-v3-performance-engineer?

It is published by ruvnet in ruvnet/ruflo. It has 1,119 recorded installs.

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