Which agent topologies are supported?

The skill supports Mesh (peer-to-peer), Hierarchical (queen-worker), and Adaptive topologies, which automatically switch based on task complexity.

Is the system resilient to agent failures?

Yes, it includes built-in fault tolerance with configurable retry strategies and automatic task reassignment to ensure workflow continuity.

How do agents share information within the swarm?

Agents utilize a unified Swarm Memory system to store and retrieve shared state, ensuring all agents have access to the necessary context and data models.

What is Swarm Orchestration?

It is a specialized skill for Claude that allows for the coordination of multiple AI agents to work together on complex tasks using various organizational patterns called topologies.

Can it handle parallel task execution?

Absolutely. It supports both parallel mode for concurrent task processing and pipeline mode for sequential tasks with specific dependencies.

Swarm Orchestration

Name: Swarm Orchestration
Author: ellisapotheosis

byellisapotheosis

데이터 과학 및 ML

Orchestrates multi-agent AI systems for parallel task execution and intelligent workflow coordination using dynamic topologies.

소개

Swarm Orchestration empowers Claude to manage complex, distributed AI workflows by coordinating multiple specialized agents within a unified system. Built on agentic-flow, it supports mesh, hierarchical, and adaptive topologies, allowing for automatic task distribution, shared memory across agents, and robust fault tolerance. This skill is ideal for large-scale software engineering tasks, complex data processing pipelines, and building autonomous multi-agent systems that require dynamic scaling and high-level coordination beyond the capabilities of a single model.

주요 기능

Parallel and Pipeline Task Execution for optimized concurrency
Comprehensive Performance Monitoring for tracking throughput and latency
Automatic Load Balancing and Fault Tolerance with task reassignment
Dynamic Topology Support including Mesh, Hierarchical, and Adaptive patterns
Shared Swarm Memory for seamless cross-agent state management
0 GitHub stars

사용 사례

Building full-stack applications using specialized coder, tester, and reviewer agents
Scaling AI development workflows through distributed task management and adaptive orchestration
Executing multi-stage data processing pipelines with sequential dependencies

소개

주요 기능

Parallel and Pipeline Task Execution for optimized concurrency
Comprehensive Performance Monitoring for tracking throughput and latency
Automatic Load Balancing and Fault Tolerance with task reassignment
Dynamic Topology Support including Mesh, Hierarchical, and Adaptive patterns
Shared Swarm Memory for seamless cross-agent state management
0 GitHub stars

사용 사례

Building full-stack applications using specialized coder, tester, and reviewer agents
Scaling AI development workflows through distributed task management and adaptive orchestration
Executing multi-stage data processing pipelines with sequential dependencies