agent-hierarchical-coordinator

Name: agent-hierarchical-coordinator
Availability: InStock
Author: ethansuttor

Coordinates hierarchical swarm agents for strategic planning, delegation, and monitoring via MCP tooling.

Updated Apr 1, 2026

Search Tags:#automation #workflow #agents #swarm #coordination #memory-management #task-decomposition

Ready to run

npx skills add https://github.com/Ethansuttor/QUANTIFIED --skill agent-hierarchical-coordinator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-hierarchical-coordinator
Source: https://github.com/Ethansuttor/QUANTIFIED/tree/main/.gemini/skills/ruflo/.agents/skills/agent-hierarchical-coordinator
Command: npx skills add https://github.com/Ethansuttor/QUANTIFIED --skill agent-hierarchical-coordinator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables centralized orchestration of hierarchical swarm agents to plan, delegate, and monitor complex multi-layered tasks, reducing coordination overhead and improving delivery consistency.

Core Features & Use Cases

Strategic task decomposition: Break down complex objectives into manageable sub-tasks and assign them to specialized workers.
Agent supervision & delegation: Spawn, monitor, and guide worker agents; handle escalations and ensure workload balance.
Coordination & memory protocol: Maintain a shared coordination state via a mandatory memory usage protocol for real-time status and progress tracking.

Quick Start

Invoke the agent-hierarchical-coordinator to initialize the swarm and begin hierarchical coordination.

Frequently Asked Questions about agent-hierarchical-coordinator

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

FAQPage Schema

How do I coordinate hierarchical swarm agents for complex task execution?▼

A queen-led hierarchy coordinates swarm agents by decomposing complex tasks into sub-tasks, delegating them to specialized workers, and monitoring execution via a shared memory protocol. This centralized orchestration reduces coordination overhead and ensures delivery consistency.

What is a memory coordination protocol for swarm agents?▼

A memory coordination protocol maintains a shared coordination state across queen and worker agents, enabling real-time status and progress tracking. It ensures all agents in the swarm access synchronized task data during hierarchical execution.

How do I decompose complex objectives into sub-tasks for specialized workers?▼

Strategic task decomposition breaks down complex objectives into manageable sub-tasks and assigns them to specialized worker agents based on their roles. The queen agent handles the decomposition while monitoring workload balance and handling escalations.

Does this hierarchical agent coordination approach work with MCP tooling?▼

Yes, hierarchical swarm coordination implements performance reporting hooks and spawn/monitor workflows via MCP tooling. This integration supports real-time monitoring and agent supervision across queen and worker roles during task execution.

When do I need a queen-led swarm architecture for agent delegation?▼

A queen-led swarm architecture is needed for multi-layered agent systems requiring strategic planning, centralized delegation, and real-time monitoring. It suits scenarios where coordination overhead is high and delivery consistency across specialized workers is critical.

What are the limitations of centralized swarm agent orchestration?▼

Centralized swarm orchestration depends on the queen agent for strategic planning and delegation, creating a coordination bottleneck if the memory protocol fails. It is less suited for decentralized tasks where autonomous worker agents operate without hierarchical supervision.