Can I add more workers without changing the Hub logic?

Yes, the architecture is designed specifically for horizontal scaling, allowing you to increase the number of spokes without modifying the central coordinator.

What is the Hub and Spoke pattern?

It is a centralized orchestration model where a single hub coordinates and delegates tasks to multiple independent 'spoke' workers for parallel execution.

How does this skill improve Claude's efficiency?

It enables Claude to handle high-volume tasks by splitting them into smaller, parallelized segments rather than processing them sequentially.

What is a common use case for this skill?

A typical use case is repository management, where the hub identifies all repos in an organization and spawns spokes to perform audits or updates on each one simultaneously.

Does the Hub perform the actual work?

No, the Hub's primary role is to distribute, track, and summarize; the actual execution of tasks is handled by the spokes.

Hub and Spoke Architecture

Name: Hub and Spoke Architecture
Author: adaptive-enforcement-lab

byadaptive-enforcement-lab

Productivity & Workflow

Orchestrates complex parallel tasks through a centralized hub and distributed worker spokes to achieve horizontal scalability.

About

The Hub and Spoke skill implements a centralized orchestration pattern designed for managing distributed execution across multiple parallel workers. It enables Claude to act as a coordinator that discovers tasks—such as multiple code repositories or data sets—and delegates them to independent spokes for execution. This separation of concerns ensures that the orchestrator remains lightweight while allowing the system to scale horizontally by adding workers without modifying the core logic, ultimately aggregating results into a cohesive summary.

Key Features

Centralized task orchestration and distribution
0 GitHub stars
Parallel execution of decoupled sub-tasks
Horizontal scaling via independent worker spokes
Automated result aggregation and summarization
Separation of coordinator logic from execution logic

Use Cases

Distributing large-scale data processing across multiple parallel workers
Orchestrating complex CI/CD pipelines with independent execution nodes
Managing multi-repository maintenance tasks simultaneously

About

Key Features

Centralized task orchestration and distribution
0 GitHub stars
Parallel execution of decoupled sub-tasks
Horizontal scaling via independent worker spokes
Automated result aggregation and summarization
Separation of coordinator logic from execution logic

Use Cases

Distributing large-scale data processing across multiple parallel workers
Orchestrating complex CI/CD pipelines with independent execution nodes
Managing multi-repository maintenance tasks simultaneously