Can I run tasks in parallel across the network?

Yes, the orchestrator can sync your codebase to multiple hosts and launch parallel tasks, which is particularly effective for running large-scale test suites simultaneously.

Does it handle code synchronization automatically?

Yes, it uses the 'dw sync' command to ensure your project directory is up-to-date on the target remote host before any gummy tasks are executed.

What infrastructure is required to use this skill?

You need a Tailscale network, the 'd' command utility, and gummy-agents installed on your remote host machines to enable distributed orchestration.

How does it decide which host to use for a task?

It executes the 'dw load' command to fetch system metrics like CPU usage and memory, then calculates a composite score to select the optimal node for the specific task type.

Can I monitor all my remote agents in one place?

The monitoring workflow aggregates the status, active sessions, and resource usage of all specialists across your network into a single unified dashboard.

Distributed Gummy Orchestrator

Name: Distributed Gummy Orchestrator
Author: Human-Frontier-Labs-Inc

byHuman-Frontier-Labs-Inc

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Deployment & DevOps

Orchestrates and load-balances AI agents across distributed networks for multi-host development and parallel testing.

About

The Distributed Gummy Orchestrator empowers Claude to coordinate complex development tasks across multiple network nodes using the 'd' command suite and gummy-agents. It intelligently analyzes real-time system metrics to select the optimal host for specific workloads, automates codebase synchronization via Tailscale, and enables parallel execution of AI specialists. This skill is ideal for developers managing high-scale environments who need to distribute resource-intensive tasks, such as database optimization or cross-platform testing, across a cluster of machines from a single interface.

Key Features

Unified network-wide monitoring and status dashboard for all active agents
Automated codebase synchronization across remote network nodes using Tailscale
Intelligent load-balanced task distribution based on real-time CPU and memory metrics
Context-aware host selection tailored to specific task requirements like I/O or CPU intensity
1 GitHub stars
Parallel execution of AI specialists for simultaneous multi-platform testing

Use Cases

Managing and monitoring a fleet of specialized AI agents across a private distributed network
Running comprehensive test suites concurrently across multiple machines to reduce CI/CD time
Distributing resource-heavy database optimizations to the least loaded server in a cluster

About

Key Features

Unified network-wide monitoring and status dashboard for all active agents
Automated codebase synchronization across remote network nodes using Tailscale
Intelligent load-balanced task distribution based on real-time CPU and memory metrics
Context-aware host selection tailored to specific task requirements like I/O or CPU intensity
1 GitHub stars
Parallel execution of AI specialists for simultaneous multi-platform testing

Use Cases

Managing and monitoring a fleet of specialized AI agents across a private distributed network
Running comprehensive test suites concurrently across multiple machines to reduce CI/CD time
Distributing resource-heavy database optimizations to the least loaded server in a cluster