How does shared memory benefit an agent swarm?

Shared memory allows different agents to store and retrieve data from a centralized state, ensuring that every agent in the swarm has the necessary context to complete their specific sub-task.

Can the swarm handle agent failures automatically?

Yes, the skill includes fault tolerance features such as exponential backoff retries and automatic task reassignment to ensure the workflow continues even if an individual agent fails.

What is adaptive execution in Swarm Orchestration?

Adaptive execution allows the system to automatically determine the most efficient topology and execution strategy based on the specific complexity and constraints of the task provided.

What is the difference between Mesh and Hierarchical topologies?

Mesh topology enables peer-to-peer communication and distributed decision-making among equal agents, while Hierarchical topology uses a centralized 'Queen' agent to coordinate specialized 'Worker' agents.

Swarm Orchestration

Name: Swarm Orchestration
Author: ellisapotheosis

byellisapotheosis

Productivity & Workflow

Orchestrates multi-agent swarms using agentic-flow for parallel task execution, dynamic topologies, and intelligent coordination.

About

Swarm Orchestration is a specialized capability that enables Claude to manage complex, distributed AI workflows by coordinating multiple specialized agents. Leveraging the agentic-flow framework, this skill allows for the creation of mesh, hierarchical, or adaptive topologies that support automatic task distribution, load balancing, and fault tolerance. It is ideal for large-scale engineering projects where high-level goals must be decomposed into parallel sub-tasks—such as simultaneous API development, UI component building, and test suite generation—while maintaining a shared global state through unified memory coordination.

Key Features

Global shared memory for seamless context exchange between agents
Automated load balancing and exponential backoff fault tolerance
Parallel and pipeline execution modes for complex task management
Support for Mesh, Hierarchical, and Adaptive swarm topologies
0 GitHub stars
Real-time performance monitoring and swarm utility metrics

Use Cases

Implementing self-healing distributed systems with automatic task reassignment
Scaling complex AI workflows that exceed the capacity of a single-agent architecture
Building full-stack applications by coordinating architect, developer, and tester agents

About

Key Features

Global shared memory for seamless context exchange between agents
Automated load balancing and exponential backoff fault tolerance
Parallel and pipeline execution modes for complex task management
Support for Mesh, Hierarchical, and Adaptive swarm topologies
0 GitHub stars
Real-time performance monitoring and swarm utility metrics

Use Cases

Implementing self-healing distributed systems with automatic task reassignment
Scaling complex AI workflows that exceed the capacity of a single-agent architecture
Building full-stack applications by coordinating architect, developer, and tester agents