zhua-distributed
爪爪分布式部署系统 —— 实现多实例协同、负载均衡、故障转移。Use when 爪爪需要分布式部署、多设备协同、或构建爪爪网络。
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/beipian261/zhua-distributedWhat This Skill Does
The zhua-distributed skill transforms your OpenClaw agent from a single-device assistant into a robust, enterprise-grade distributed network. It enables the creation of a 'Zhua Network' where tasks are intelligently partitioned across multiple instances. By implementing a Master-Slave architecture, it provides load balancing, high availability via fault tolerance, and shared intelligence through memory synchronization. Whether you are scaling to handle intensive computations or require a presence across multiple physical or virtual nodes, this skill orchestrates the entire cluster.
Installation
To install the zhua-distributed skill, execute the following command in your terminal:
clawhub install openclaw/skills/skills/beipian261/zhua-distributed
Once installed, ensure your environment variables are configured for node discovery and synchronization, and initialize your master instance using the scripts/init_master.py utility. Verify cluster connectivity by listing slave instances before launching your first distributed task.
Use Cases
- High-Performance Computation: Offload heavy AI processing or data analysis tasks to a farm of compute-optimized instances while keeping the master instance responsive for user input.
- Multi-Location Availability: Deploy agents in different data centers or physical locations to ensure that your assistant remains operational even if one region experiences downtime.
- Scalable Ecosystems: Build a comprehensive 'Zhua' network that manages memory storage, long-running background tasks, and real-time interaction simultaneously across dedicated hardware roles.
Example Prompts
- 'Zhua, initialize a master node on the local server and prepare the cluster for distributed tasks.'
- 'Add the remote workstation at 192.168.1.50 as a new compute instance to my current Zhua network.'
- 'Distribute the current data processing task across all active compute instances and ensure memory states are synced.'
Tips & Limitations
- Connectivity: Ensure that all nodes can communicate over the specified internal network ports. Firewalls may require explicit rule updates for the sync layer.
- Data Conflicts: While the system uses timestamp-based resolution for data sync, it is best practice to avoid concurrent writes to the same memory segment from multiple instances.
- Network Dependency: If the master node goes offline, the network will enter a degraded mode; ensure you have a monitoring strategy to restart the master process if needed.
- Scalability: While the architecture supports N nodes, maintain a reasonable ratio of compute nodes to memory storage nodes to prevent bottlenecking at the storage layer.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-beipian261-zhua-distributed": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, code-execution
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