Atlas / Skills / ruvnet / Agent V3 Performance Engineer

Agent V3 Performance EngineerCAUTION

skills/ruvnet/agent-v3-performance-engineer

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Verdict
CAUTION
Grade
B
Trust score
89 /100
Version
β€”
Hosts
β€”
License
MIT
Stars
73,410
01

Overview

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Read from source at commit 3e0c089e8335OBSERVED Β· 2026-09-28
02

What it tells the agent

The instruction file, verbatim from the audited commit β€” this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: agent-v3-performance-engineer
description: Agent skill for v3-performance-engineer - invoke with $agent-v3-performance-engineer
---

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

    # Check performance tools
    command -v npm &>$dev$null && echo "πŸ“¦ npm available for benchmarking"
    command -v node &>$dev$null && node --version | xargs echo "πŸš€ Node.js:"

    echo "πŸ”¬ 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**
```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚           FLASH ATTENTION               β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  Baseline: Standard attention mechanism β”‚
β”‚  Target:   2.49x - 7.47x speedup       β”‚
β”‚  Memory:   50-75% reduction             β”‚
β”‚  Method:   agentic-flow@alpha integrationβ”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```

### **Search Performance Revolution**
```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚            SEARCH OPTIMIZATION         β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  Current:  O(n) linear search           β”‚
β”‚  Target:   150x - 12,500x improvement   β”‚
β”‚  Method:   AgentDB HNSW indexing        β”‚
β”‚  Latency:  Sub-100ms for 1M+ entries    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```

### **System-Wide Optimization**
```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚          SYSTEM PERFORMANCE             β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  Startup:    <500ms (cold start)        β”‚
β”‚  Memory:     50-75% reduction           β”‚
β”‚  SONA:       <0.05ms adaptation         β”‚
β”‚  Code Size:  <5k lines (vs 15k+)       β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```

## 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() - d
03

Trust audit

CAUTIONgrade B Β· trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (4)

MEDIUMInventory / provenance Β· inv.symlink Β· CWE-1104
crates
crates
Why it matters. link not followed
MEDIUMInventory / provenance Β· inv.symlink Β· CWE-1104
plugin/agents
plugin/agents
Why it matters. link not followed
MEDIUMInventory / provenance Β· inv.symlink Β· CWE-1104
plugin/commands
plugin/commands
Why it matters. link not followed
MEDIUMInventory / provenance Β· inv.symlink Β· CWE-1104
plugin/skills
plugin/skills
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-09-28 Β· audit v0.4.1 Β· source sha 3e0c089e8335full audit observations/trust-audit/skill/ruvnet__agent-v3-performance-engineer.json Β· Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-09-283e0c089e8335CAUTIONB89first audit
05

Questions

What does the Agent V3 Performance Engineer skill do?

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Is Agent V3 Performance Engineer safe to install?

With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.

What can Agent V3 Performance Engineer access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (3e0c089e8335), read on 2026-09-28. The repository is watched, and a new audit runs when it changes β€” this is the first audit.

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