V3 Performance Optimization 精选
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
安装方式
cp -r .claude/skills/v3-performance-optimization ~/.claude/skills/v3-performance-optimization把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同,Claude Code 默认是 ~/.claude/skills/
/plugin marketplace add ruvnet/RuView⚠️ 该命令形态随客户端版本变化,仅在检测到 .claude-plugin/marketplace.json 时展示;不可用时请用「目录复制」。
https://raw.githubusercontent.com/ruvnet/RuView/a71634ae8a606bba5463489450c69e8b42de114e/.claude/skills/v3-performance-optimization/SKILL.md下载后另存为 ~/.claude/skills/v3-performance-optimization/SKILL.md。链接锁定采集时的 commit,内容与本站展示一致。
整仓一次性获取:git clone --depth 1 https://github.com/ruvnet/RuView.git
依赖与权限
- 无需 API Key
- 无需联网
- 依赖外部命令:
npm - 以上为按正文与 frontmatter 自动推断的信号,仅供参考;安装前建议先阅读原文。
SKILL.md 正文
V3 Performance Optimization
What This Skill Does
Validates and optimizes claude-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.
Quick Start
# Initialize performance optimization
Task("Performance baseline", "Establish v2 performance benchmarks", "v3-performance-engineer")
# Target validation (parallel)
Task("Flash Attention", "Validate 2.49x-7.47x speedup target", "v3-performance-engineer")
Task("Search optimization", "Validate 150x-12,500x search improvement", "v3-performance-engineer")
Task("Memory optimization", "Achieve 50-75% memory reduction", "v3-performance-engineer")
Performance Target Matrix
Flash Attention Revolution
┌─────────────────────────────────────────┐
│ FLASH ATTENTION │
├─────────────────────────────────────────┤
│ Baseline: Standard attention │
│ Target: 2.49x - 7.47x speedup │
│ Memory: 50-75% reduction │
│ Latency: Sub-millisecond processing │
└─────────────────────────────────────────┘
Search Performance Revolution
┌─────────────────────────────────────────┐
│ SEARCH OPTIMIZATION │
├─────────────────────────────────────────┤
│ Current: O(n) linear search │
│ Target: 150x - 12,500x improvement │
│ Method: HNSW indexing │
│ Latency: <100ms for 1M+ entries │
└─────────────────────────────────────────┘
Comprehensive Benchmark Suite
Startup Performance
class StartupBenchmarks {
async benchmarkColdStart(): Promise<BenchmarkResult> {
const startTime = performance.now();
await this.initializeCLI();
await this.initializeMCPServer();
await this.spawnTestAgent();
const totalTime = performance.now() - startTime;
return {
total: totalTime,
target: 500, // ms
achieved: totalTime < 500
};
}
}
Memory Operation Benchmarks
class MemoryBenchmarks {
async benchmarkVectorSearch(): Promise<SearchBenchmark> {
const queries = this.generateTestQueries(10000);
// Baseline: Current linear search
const baselineTime = await this.timeOperation(() =>
this.currentMemory.searchAll(queries)
);
// Target: HNSW search
const hnswTime = await this.timeOperation(() =>
this.agentDBMemory.hnswSearchAll(queries)
);
const improvement = baselineTime / hnswTime;
return {
baseline: baselineTime,
hnsw: hnswTime,
improvement,
targetRange: [150, 12500],
achieved: improvement >= 150
};
}
async benchmarkMemoryUsage(): Promise<MemoryBenchmark> {
const baseline = process.memoryUsage().heapUsed;
await this.loadTestDataset();
const withData = process.memoryUsage().heapUsed;
await this.enableOptimization();
const optimized = process.memoryUsage().heapUsed;
const reduction = (withData - optimized) / withData;
return {
baseline,
withData,
optimized,
reductionPercent: reduction * 100,
targetReduction: [50, 75],
achieved: reduction >= 0.5
};
}
}
Swarm Coordination Benchmarks
class SwarmBenchmarks {
async benchmark15AgentCoordination(): Promise<SwarmBenchmark> {
const agents = await this.spawn15Agents();
// Coordination latency
const coordinationTime = await this.timeOperation(() =>
this.coordinateSwarmTask(agents)
);
// Task decomposition
const decompositionTime = await this.timeOperation(() =>
this.decomposeComplexTask()
);
// Consensus achievement
const consensusTime = await this.timeOperation(() =>
this.achieveSwarmConsensus(agents)
);
return {
coordination: coordinationTime,
decomposition: decompositionTime,
consensus: consensusTime,
agentCount: 15,
efficiency: this.calculateEfficiency(agents)
};
}
}
Flash Attention Benchmarks
class AttentionBenchmarks {
async benchmarkFlashAttention(): Promise<AttentionBenchmark> {
const sequences = this.generateSequences([512, 1024, 2048, 4096]);
const results = [];
for (const sequence of sequences) {
// Baseline attention
const baselineResult = await this.benchmarkStandardAttention(sequence);
// Flash attention
const flashResult = await this.benchmarkFlashAttention(sequence);
results.push({
sequenceLength: sequence.length,
speedup: baselineResult.time / flashResult.time,
memoryReduction: (baselineResult.memory - flashResult.memory) / baselineResult.memory,
targetSpeedup: [2.49, 7.47],
achieved: this.checkTarget(flashResult, [2.49, 7.47])
});
}
return {
results,
averageSpeedup: this.calculateAverage(results, 'speedup'),
averageMemoryReduction: this.calculateAverage(results, 'memoryReduction')
};
}
}
SONA Learning Benchmarks
class SONABenchmarks {
async benchmarkAdaptationTime(): Promise<SONABenchmark> {
const scenarios = [
'pattern_recognition',
'task_optimization',
'error_correction',
'performance_tuning'
];
const results = [];
for (const scenario of scenarios) {
const startTime = performance.hrtime.bigint();
await this.sona.adapt(scenario);
const endTime = performance.hrtime.bigint();
const adaptationTimeMs = Number(endTime - startTime) / 1000000;
results.push({
scenario,
adaptationTime: adaptationTimeMs,
target: 0.05, // ms
achieved: adaptationTimeMs <= 0.05
});
}
return {
scenarios: results,
averageTime: results.reduce((sum, r) => sum + r.adaptationTime, 0) / results.length,
successRate: results.filter(r => r.achieved).length / results.length
};
}
}
Performance Monitoring Dashboard
Real-time Metrics
class PerformanceMonitor {
async collectMetrics(): Promise<PerformanceSnapshot> {
return {
timestamp: Date.now(),
flashAttention: await this.measureFlashAttention(),
searchPerformance: await this.measureSearchSpeed(),
memoryUsage: await this.measureMemoryEfficiency(),
startupTime: await this.measureStartupLatency(),
sonaAdaptation: await this.measureSONASpeed(),
swarmCoordination: await this.measureSwarmEfficiency()
};
}
async generateReport(): Promise<PerformanceReport> {
const snapshot = await this.collectMetrics();
return {
summary: this.generateSummary(snapshot),
achievements: this.checkTargetAchievements(snapshot),
trends: this.analyzeTrends(),
recommendations: this.generateOptimizations(),
regressions: await this.detectRegressions()
};
}
}
Continuous Regression Detection
class PerformanceRegression {
async detectRegressions(): Promise<RegressionReport> {
const current = await this.runFullBenchmark();
const baseline = await this.getBaseline();
const regressions = [];
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,
severity: this.classifyRegression(change)
});
}
}
return {
hasRegressions: regressions.length > 0,
regressions,
recommendations: this.generateRegressionFixes(regressions)
};
}
}
Optimization Strategies
Memory Optimization
class MemoryOptimization {
async optimizeMemoryUsage(): Promise<OptimizationResult> {
// Implement memory pooling
await this.setupMemoryPools();
// Enable garbage collection tuning
await this.optimizeGarbageCollection();
// Implement object reuse patterns
await this.setupObjectPools();
// Enable memory compression
await this.enableMemoryCompression();
return this.validateMemoryReduction();
}
}
CPU Optimization
class CPUOptimization {
async optimizeCPUUsage(): Promise<OptimizationResult> {
// Implement worker thread pools
await this.setupWorkerThreads();
// Enable CPU-specific optimizations
await this.enableSIMDInstructions();
// Implement task batching
await this.optimizeTaskBatching();
return this.validateCPUImprovement();
}
}
Target Validation Framework
Performance Gates
class PerformanceGates {
async validateAllTargets(): Promise<ValidationReport> {
const results = await Promise.all([
this.validateFlashAttention(), // 2.49x-7.47x
this.validateSearchPerformance(), // 150x-12,500x
this.validateMemoryReduction(), // 50-75%
this.validateStartupTime(), // <500ms
this.validateSONAAdaptation() // <0.05ms
]);
return {
allTargetsAchieved: results.every(r => r.achieved),
results,
overallScore: this.calculateOverallScore(results),
recommendations: this.generateRecommendations(results)
};
}
}
Success Metrics
Primary Targets
- [ ] Flash Attention: 2.49x-7.47x speedup validated
- [ ] Search Performance: 150x-12,500x improvement confirmed
- [ ] Memory Reduction: 50-75% usage optimization achieved
- [ ] Startup Time: <500ms cold start consistently
- [ ] SONA Adaptation: <0.05ms learning response time
- [ ] 15-Agent Coordination: Efficient parallel execution
Continuous Monitoring
- [ ] Performance Dashboard: Real-time metrics collection
- [ ] Regression Testing: Automated performance validation
- [ ] Trend Analysis: Performance evolution tracking
- [ ] Alert System: Immediate regression notification
Related V3 Skills
v3-integration-deep- Performance integration with agentic-flowv3-memory-unification- Memory performance optimizationv3-swarm-coordination- Swarm performance coordinationv3-security-overhaul- Secure performance patterns
Usage Examples
Complete Performance Validation
# Full performance suite
npm run benchmark:v3
# Specific target validation
npm run benchmark:flash-attention
npm run benchmark:agentdb-search
npm run benchmark:memory-optimization
# Continuous monitoring
npm run monitor:performance质量与来源
- 所属仓库
- ruvnet/RuView
- 仓库星标
- 9.8 万
- 最近更新
- 今天
- 采集时间
- 今天
- commit
- a71634ae8a60
- 内容指纹
- sha256:25858e4c4325f0d
- 文件路径
- .claude/skills/v3-performance-optimization/SKILL.md
同仓库其它 Skill
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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.
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.
AgentDB Performance 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.
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.
browser
Web browser automation with AI-optimized snapshots for claude-flow agents
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