worker-benchmarks
🎯Skillfrom ruvnet/claude-flow
|Benchmarks and measures performance metrics for distributed AI worker nodes in the Claude-Flow platform, evaluating computational efficiency and resource utilization.
Installation
npx skills add https://github.com/ruvnet/claude-flow --skill worker-benchmarksSkill Details
Overview
# 🌊 Claude-Flow v3: Enterprise AI Orchestration Platform
 [](https://github.com/ruvnet/claude-flow) [](https://www.npmjs.com/package/claude-flow) [](https://www.npmjs.com/package/claude-flow) [](https://ruv.io) [](https://discord.com/invite/dfxmpwkG2D) [](https://github.com/ruvnet/claude-flow) [](https://opensource.org/licenses/MIT) --- [](https://x.com/ruv) [](https://www.linkedin.com/in/reuvencohen/) [](https://www.youtube.com/@ReuvenCohen) # Production-ready multi-agent AI orchestration for Claude Code Deploy 60+ specialized agents in coordinated swarms with self-learning capabilities, fault-tolerant consensus, and enterprise-grade security.
Getting into the Flow
Claude-Flow is a comprehensive AI agent orchestration framework that transforms Claude Code into a powerful multi-agent development platform. It enables teams to deploy, coordinate, and optimize specialized AI agents working together on complex software engineering tasks.
Self-Learning/Self-Optimizing Agent Architecture
```
User → Claude-Flow (CLI/MCP) → Router → Swarm → Agents → Memory → LLM Providers
↑ ↓
└──── Learning Loop ←──────┘
```
```mermaid
flowchart TB
subgraph USER["👤 User Layer"]
U[User]
end
subgraph ENTRY["🚪 Entry Layer"]
CLI[CLI / MCP Server]
AID[AIDefence Security]
end
subgraph ROUTING["🧭 Routing Layer"]
QL[Q-Learning Router]
MOE[MoE - 8 Experts]
SK[Skills - 42+]
HK[Hooks - 17]
end
subgraph SWARM["🐝 Swarm Coordination"]
TOPO[Topologies
mesh/hier/ring/star]
CONS[Consensus
Raft/BFT/Gossip/CRDT]
CLM[Claims
Human-Agent Coord]
end
subgraph AGENTS["🤖 60+ Agents"]
AG1[coder]
AG2[tester]
AG3[reviewer]
AG4[architect]
AG5[security]
AG6[...]
end
subgraph RESOURCES["📦 Resources"]
MEM[(Memory
AgentDB)]
PROV[Providers
Claude/GPT/Gemini/Ollama]
WORK[Workers - 12
ultralearn/audit/optimize]
end
subgraph RUVECTOR["🧠 RuVector Intelligence Layer"]
direction TB
subgraph ROW1[" "]
SONA[SONA
Self-Optimize
<0.05ms]
EWC[EWC+
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