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orchestrator

Route ambiguous triggers to the appropriate agent-loop pattern.

5|3|Updated Feb 21, 2026
Search Tags:#automation#routing#delegation#orchestrator#agent-loop#inner-loop#outer-loop
One-click install
npx skills add https://github.com/richfrem/agent-plugins-skills --skill orchestrator-richfrem
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: orchestrator
Source: https://github.com/richfrem/agent-plugins-skills/tree/main/plugins/agent-loops/skills/orchestrator
Command: npx skills add https://github.com/richfrem/agent-plugins-skills --skill orchestrator-richfrem

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Analyzes an ambiguous trigger and routes it to the correct specialized agent-loop implementation, enabling seamless selection between learning loops, red-team reviews, dual-loop delegation, parallel swarm, or triple-loop patterns while managing shared closure: seal, persist, retrospective, and self-improvement.

Core Features & Use Cases

  • Route triggers to the appropriate agent-loop pattern based on task context to determine whether to run a simple learning loop, a red-team review, dual-loop delegation, a parallel swarm, or a triple-loop learning setup.
  • Enforce a structured lifecycle: planning, delegation, execution, verification, retrospective, and handoff to the primary agent for ecosystem sealing.
  • Operate in isolation with no hard-dependencies on sibling plugins and minimal external dependencies; supports command-line invocation of agent_orchestrator.py.

Quick Start

Provide a trigger prompt and let the orchestrator route it to the correct agent-loop pattern.

Frequently Asked Questions about orchestrator

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

FAQPage Schema
How do I route ambiguous triggers to the correct agent loop pattern?▼

Agent loop routing analyzes an ambiguous trigger and directs it to the appropriate specialized pattern, such as a learning loop, red-team review, dual-loop delegation, parallel swarm, or triple-loop setup based on task context.

What is the best way to automate structured delegation across multiple agent loops?▼

Automating structured delegation requires enforcing a lifecycle of planning, delegation, execution, verification, retrospective, and handoff to the primary agent for ecosystem sealing across diverse task contexts.

When do I need a triple-loop or parallel swarm pattern for agent delegation?▼

You need a triple-loop or parallel swarm pattern when an ambiguous trigger demands complex task contexts, requiring advanced multi-agent coordination to execute specialized parallel workflows and shared closure.

Does this agent orchestrator require any external dependencies or sibling plugins?▼

The agent orchestrator operates in isolation with no hard dependencies on sibling plugins and minimal external dependencies, supporting direct command-line invocation of agent_orchestrator.py for routing.

How does the orchestrator handle shared closure after agent execution?▼

Shared closure is managed by sealing the ecosystem through structured persistence, retrospective analysis, self-improvement, and final handoff to the primary agent after execution and verification.

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