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V3 Performance Optimization

Benchmarks and validates claude-flow v3 performance targets including Flash Attention speedup and memory reduction.

1|1|Updated Nov 28, 2025
Search Tags:#benchmarking#performance optimization#hnsw#memory reduction#regression detection#claude-flow#flash attention
One-click install
npx skills add https://github.com/33may/robotics --skill v3-performance-optimization-33may
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: V3 Performance Optimization
Source: https://github.com/33may/robotics/tree/main/humanoid/.claude/skills/v3-performance-optimization
Command: npx skills add https://github.com/33may/robotics --skill v3-performance-optimization-33may

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Validating that claude-flow v3 meets its aggressive performance targets (2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvement, 50-75% memory reduction) requires systematic benchmarking, regression detection, and optimization workflows that are tedious to run manually. ## Core Features & Use Cases - Comprehensive Benchmark Suites: TypeScript benchmark classes for startup time, vector search, memory usage, swarm coordination, Flash Attention, and SONA adaptation speed. - Performance Monitoring & Regression Detection: Real-time metrics collection, trend analysis, and automated detection of regressions beyond a 5% threshold. - Target Validation Gates: A validation framework that checks all performance targets and generates recommendations when targets are missed. - Use Case: Before a v3 release, run the full benchmark suite to confirm Flash Attention speedup, HNSW search improvement, and memory reduction targets are all achieved, and get alerted to any regressions. ## Quick Start Ask the AI to establish v2 performance baselines and validate the Flash Attention, search, and memory optimization targets for claude-flow v3.

Frequently Asked Questions about V3 Performance Optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark Flash Attention speedup in claude-flow v3?▼

Use the AttentionBenchmarks class to compare standard attention against Flash Attention across sequence lengths of 512 to 4096. It reports speedup ratios and memory reduction, checking results against the 2.49x-7.47x target range.

How to validate HNSW search performance improvements?▼

The MemoryBenchmarks class times baseline linear search against AgentDB HNSW search over 10,000 generated queries. It computes the improvement factor and validates it against the 150x-12,500x target range.

What performance targets does claude-flow v3 need to meet?▼

Targets include 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvement, 50-75% memory reduction, under 500ms cold start, under 0.05ms SONA adaptation, and efficient 15-agent swarm coordination.

How does performance regression detection work?▼

The PerformanceRegression class compares current benchmark results against a stored baseline for each metric. Any metric degrading by more than 5% is flagged as a regression with severity classification and fix recommendations.

What are the limitations of these performance benchmarks?▼

The benchmark classes are TypeScript templates that depend on claude-flow v3 internals like AgentDB, SONA, and swarm coordination being implemented. Results vary by hardware, dataset size, and runtime environment, so targets may not be achievable in all deployments.

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