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consensus-loop:orchestrator

Orchestrate distributed task execution across parallel agents using handoff data.

15|4|Updated Mar 15, 2026
Search Tags:#workflow#audit#multi-agent#consensus#orchestrator#task-distribution
One-click install
npx skills add https://github.com/berrzebb/consensus-loop --skill consensus-loop-orchestrator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: consensus-loop:orchestrator
Source: https://github.com/berrzebb/consensus-loop/tree/main/skills/orchestrator
Command: npx skills add https://github.com/berrzebb/consensus-loop --skill consensus-loop-orchestrator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the challenge of coordinating multiple AI agents to execute distributed software delivery workflows, ensuring no unreviewed work ships and that tasks progress through a controlled lifecycle.

Core Features & Use Cases

  • Orchestrates task distribution across parallel agents based on handoff data.
  • Tracks agent assignments, dependencies, and correction cycles via SendMessage.
  • Supports both interactive and headless operation modes with phase-driven references.
  • Triggers retrospective and merge workflows after consensus is reached.

Quick Start

Start a work session by providing a handoff and let the orchestrator distribute tasks to parallel workers.

Frequently Asked Questions about consensus-loop:orchestrator

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

FAQPage Schema
How do I coordinate multiple AI agents for parallel task execution?▼

Multi-agent consensus orchestration reads handoff data, selects unblocked tasks, and delegates work to parallel agents. It tracks assignments and enforces dependency ordering to ensure tasks progress through a controlled lifecycle without unreviewed work shipping.

How does a multi-agent orchestrator handle correction cycles and task dependencies?▼

The orchestrator handles correction cycles via SendMessage to route feedback to assigned agents. It enforces dependency ordering by reading handoff data and selecting only unblocked tasks for delegation to parallel workers.

Can I run multi-agent consensus workflows in headless mode without interactive input?▼

Yes, multi-agent consensus workflows support headless operation without interactive input. The orchestrator operates in both interactive and headless modes, using phase-specific references to guide task distribution and agent lifecycle management autonomously.

What happens after parallel agents reach consensus on distributed tasks?▼

After parallel agents reach consensus on distributed tasks, the orchestrator triggers retrospective and merge workflows. This ensures completed work is reviewed and integrated, maintaining controlled lifecycle management across the software delivery process.

What is the best way to distribute tasks to parallel AI agents without shipping unreviewed work?▼

Distributing tasks to parallel AI agents without shipping unreviewed work requires an orchestrator that reads handoff data, tracks assignments, and manages correction cycles. This controlled lifecycle ensures unreviewed work never ships while tasks progress through dependency-ordered phases.

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