Can it help prevent agent loops or deadlocks?

Yes, the skill includes troubleshooting and design patterns for adding timeouts, detecting cycles, and implementing circuit breakers to ensure system stability.

What frameworks does this skill support?

The multi-agent skill provides implementation patterns and best practices for leading frameworks including LangGraph, AutoGen, and CrewAI.

How does this skill help optimize model costs?

It provides a strategic model allocation guide, suggesting high-reasoning models like Opus for orchestration while leveraging faster, cost-effective models like Haiku for simpler worker tasks.

When should I use the multi-agent skill over a single agent?

Invoke this skill when a task is too complex for a single agent, requires specialized domain knowledge, or benefits from parallel execution and hierarchical supervision.

What architecture patterns are supported?

It supports three primary patterns: Orchestrator-Worker for central control, Hierarchical for complex management structures, and Peer-to-Peer for collaborative agent interactions.

Multi-Agent Systems Builder

Name: Multi-Agent Systems Builder
Author: pluginagentmarketplace

bypluginagentmarketplace

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데이터 과학 및 ML

Orchestrates complex multi-agent workflows and distributed AI architectures for specialized task execution.

소개

The Multi-Agent skill empowers developers to build sophisticated systems where multiple AI agents collaborate to solve complex, multi-faceted problems. It provides standardized implementation patterns for orchestrator-worker, hierarchical, and peer-to-peer architectures using popular frameworks like LangGraph, AutoGen, and CrewAI. By guiding users through intelligent model allocation strategies and coordination best practices, this skill ensures robust communication, shared state management, and efficient error handling across distributed agentic workflows.

주요 기능

Standardized patterns for Orchestrator-Worker and Hierarchical architectures
1 GitHub stars
Strategic model allocation across Anthropic's Claude 3.5 family
Framework-specific templates for LangGraph, AutoGen, and CrewAI
Resiliency patterns like circuit breakers and deadlock detection
Advanced coordination logic including state synchronization and checkpointing

사용 사례

Developing autonomous software engineering teams with specialized agent roles
Building multi-step research and content generation pipelines
Implementing complex data analysis workflows requiring parallel processing

소개

주요 기능

Standardized patterns for Orchestrator-Worker and Hierarchical architectures
1 GitHub stars
Strategic model allocation across Anthropic's Claude 3.5 family
Framework-specific templates for LangGraph, AutoGen, and CrewAI
Resiliency patterns like circuit breakers and deadlock detection
Advanced coordination logic including state synchronization and checkpointing

사용 사례

Developing autonomous software engineering teams with specialized agent roles
Building multi-step research and content generation pipelines
Implementing complex data analysis workflows requiring parallel processing