orchestrator

Route tasks to capable agents with confidence scoring and checkpoints.

1|Updated Apr 23, 2026
One-click install
npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill orchestrator-mtsatryan
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: orchestrator
Source: https://github.com/mtsatryan/openclaw-ai-agents/tree/main/orchestrator
Command: npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill orchestrator-mtsatryan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The orchestrator provides a centralized, intelligent way to coordinate multiple AI agents, ensuring tasks are routed to the right specialists, plans are formed with checkpoints, and progress is monitored with human-in-the-loop validation.

Core Features & Use Cases

  • Smart routing with confidence scoring to select the best agents for a given task.
  • Sequential, parallel, and hybrid execution patterns with clear synchronization points.
  • Built-in reflection, validation, and checkpoints to ensure quality and safety.
  • Human-in-the-loop checkpoints for user decisions at critical milestones.
  • Auto-checkpoints and disaster-recovery supports to protect progress across phases.
  • Use cases include complex product development, incident response orchestration, and cross-domain project management.

Quick Start

Describe your complex task and I will generate a phased, checkpointed multi-agent plan.

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 routing?

Multi-agent AI workflow coordination uses a master router to direct tasks to the most capable agents using confidence scoring. It generates a phased plan with checkpoints, supports parallel execution, and monitors progress through human-in-the-loop validation.

What is human-in-the-loop validation in AI workflow planning?

Human-in-the-loop validation in AI workflow planning inserts user decision checkpoints at critical milestones. It ensures quality and safety by pausing execution for manual review, enabling reflection-based validation before downstream agents proceed.

Can I execute parallel and sequential patterns in multi-agent AI workflows?

You can execute parallel, sequential, and hybrid patterns in multi-agent AI workflows with clear synchronization points. The orchestrator manages these coordination patterns to handle complex tasks requiring dynamic agent selection across different execution phases.

How do I add checkpoints and disaster recovery to agent coordination?

Adding checkpoints and disaster recovery to agent coordination involves auto-checkpoints that protect progress across phases. The system applies reflection-based execution and robust coordination patterns to recover from failures and safeguard workflow state.

Does multi-agent orchestration support cross-domain project management?

Multi-agent orchestration supports cross-domain project management, incident response orchestration, and complex product development. It routes specialized tasks to capable agents with confidence scoring, forming structured plans with quality assurance checkpoints.

When should I use a master router for multi-agent workflows?

Use a master router for multi-agent workflows when tasks require dynamic agent selection, parallel execution, and human-in-the-loop checkpoints. It is ideal for complex scenarios needing intelligent routing, plan creation, and reflection-based quality assurance.