orchestrator

Name: orchestrator
Availability: InStock
Author: kardelitaitu

Coordinate multi-agent tasks by delegating to specialized agents and aggregating results.

Updated Feb 13, 2026

Search Tags:#patterns #multi-agent #orchestrate #task-delegation #fault-tolerance #load-balancing #inter-agent-communication

Ready to run

npx skills add https://github.com/kardelitaitu/auto-ai --skill orchestrator-kardelitaitu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: orchestrator
Source: https://github.com/kardelitaitu/auto-ai/tree/main/.agents/skills/orchestrator
Command: npx skills add https://github.com/kardelitaitu/auto-ai --skill orchestrator-kardelitaitu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates and delegates tasks across multiple specialized AI agents to enable faster, more reliable outcomes through orchestration and result aggregation.

Core Features & Use Cases

Central coordination with capability-aware delegation to worker agents
Support for established patterns: Supervisor, Pipeline, Fan-Out/Fan-In, and Message Passing
Fault tolerance via retries, fallbacks, load balancing, and health monitoring
Use cases spanning multi-step workflows, coordinated decision making, and hierarchical task management

Quick Start

Instantiate the orchestrator, register three specialized agents, and delegate a task to see coordinated execution.

Frequently Asked Questions about orchestrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I coordinate multiple AI agents for complex task delegation?▼

To coordinate multi-agent tasks, you can use a central orchestrator that delegates to specialized agents and aggregates results. This enables capability-aware task distribution across multiple workers for faster, reliable outcomes.

What patterns are used for fan-out and fan-in task distribution across agents?▼

Fan-out and fan-in task distribution utilizes patterns like Supervisor, Pipeline, and Message Passing. These patterns enable central coordination with worker agents, allowing parallel task execution and result aggregation.

How can I add fault tolerance and retries to my multi-agent workflow?▼

You can add fault tolerance to multi-agent workflows by applying built-in validation, retries, fallback strategies, and load balancing. This ensures reliable execution even when individual agents fail during task processing.

Can I use a pipeline pattern for inter-agent communication and result aggregation?▼

Yes, a pipeline pattern supports inter-agent communication and result aggregation. It allows sequential task processing where specialized agents pass intermediate results to the next stage for coordinated execution.

What's the best way to handle load balancing and health monitoring for specialized agents?▼

The best way to handle load balancing and health monitoring is through an orchestration layer that tracks agent capabilities and health. It dynamically delegates tasks to available workers, ensuring optimal resource utilization.

When do I need a supervisor pattern for hierarchical task management?▼

You need a supervisor pattern for hierarchical task management when coordinating complex multi-step workflows. It provides central coordination, delegating specialized subtasks to worker agents and aggregating their final results.