How does this skill prevent race conditions and merge conflicts between multiple AI agents?

This library provides the architectural blueprints and YAML schemas needed to prevent agents from overwriting each other's code, managing task dependencies and state transitions through a structured tmux environment.

Which specific AI agents are supported by this orchestration framework?

The library is designed for any terminal-based AI agent, such as Claude Code, but is compatible with any agent capable of executing shell commands and reading YAML configuration files.

What specific assets are included in the purchase of the Master Library?

You will receive a comprehensive suite of YAML task queue templates, tmux session configuration scripts, and the state-management logic required to coordinate complex agent workflows.

Does this framework require a cloud-based dashboard or external database to track task states?

No, this skill relies on local file-system tracking and tmux session management, making it ideal for developers who want to maintain full control over their workflow without external API dependencies.

Is there a limit to the number of agents I can orchestrate simultaneously using this library?

While it excels at managing up to 8+ parallel workers, the framework is highly scalable; your primary constraint will be your system's hardware resources and the rate limits of the AI models you are utilizing.

Multi Agent Orchestration Master Library

Example session with this skill installed

I need to refactor 20 legacy modules. Design a 4-worker tmux orchestration plan using a YAML queue to track progress and prevent agents from editing the same files simultaneously.

Read your context and instructions
Compiled the multi agent orchestration

I have designed a multi-agent orchestration plan for your 20-module refactor. This setup uses a central task queue to serialize state and tmux panes to isolate worker processes.

Orchestration Plan: Legacy Refactor

1. Task Queue Schema (tasks.yaml)

version: 1
tasks:
  - id: REF-001
    title: "Refactor auth_service.py"
    status: pending
    priority: high
    owner: null
    depends_on: []
    acceptance_criteria:
      - "Type hints added"
      - "Pytest coverage > 90%"
    attempts: 0
  - id: REF-002
    title: "Refactor payment_gateway.py"
    status: pending
    priority: high
    owner: null
    depends_on: [REF-001]
    acceptance_criteria:
      - "Interface matches new Auth contract"
    attempts: 0

2. tmux Worker Layout


# Launch orchestration session
tmux new-session -d -s refactor-orbit
tmux rename-window -t refactor-orbit:0 'coordinator'
tmux new-window -t refactor-orbit:1 -n 'workers'

# Split into 4 panes for workers
tmux select-window -t refactor-orbit:1
tmux split-window -h
tmux select-pane -t 0
tmux split-window -v
tmux select-pane -t 2
tmux split-window -v

# Command to launch worker in each pane
# usage: ./worker.sh --id worker-01 --queue tasks.yaml

3. State Transition Table

Current Status	Event	Next Status	Action
pending	task.assigned	assigned	Worker claims ID in YAML
assigned	task.started	running	Worker creates git worktree
running	task.completed	review	Run `pytest` and `lint`
review	review.approved	completed	Merge worktree to main

Next steps

Initialize the tasks.yaml file with the first 5 modules to validate worker behavior.
Create the worker.sh wrapper script to handle YAML updates using yq.
Verify your environment has git worktree support to ensure file isolation.

1. Task Queue Schema (tasks.yaml

Connects securely to your tools. The creator never sees your data.

About this skill

Managing multiple AI agents in a single repository often leads to race conditions, conflicting code edits, and wasted tokens. This skill provides the architectural framework needed to orchestrate coordinated terminal-based AI coding workflows.

What it does

Task Decomposition breaks complex requests into bounded, independent tasks with clear deliverables.
State Management tracks task status, ownership, and dependencies using a defined YAML queue or state file.
Concurrency Control manages parallel workers using tmux session layouts and worktree isolation to prevent merge conflicts.
Event Orchestration defines communication contracts for task assignments, heartbeats, and completion signals.
Verification Gating enforces automated checks, linting, and manual approval before tasks are marked complete.

How it works

Define the workload by identifying task categories, shared file dependencies, and quality requirements.
Configure the queue using the provided YAML schema to set priorities and dependency graphs.
Initialize workers via tmux pane layouts or separate branch worktrees for isolated execution.
Monitor transitions as tasks move from pending to running, review, and completion based on event contracts.

Frameworks & tools

Works with Claude Code, tmux, YAML-based task queues, and standard Git workflows. Compatible with any terminal-based environment supporting file-system state tracking and shell scripting.

Why this beats prompting it yourself

Generic prompts fail to handle state persistence or resource contention between parallel agents. This skill provides a formal schema for task ownership and recovery policies that prevent agents from overwriting each other's work or getting stuck in infinite retry loops.

Use cases

Coordinating 8+ parallel workers for large-scale refactoring projects.
Managing long-running CI/CD migration tasks with explicit dependency tracking.
Implementing an event-driven review loop where one agent writes tests and another implements features.

Known limitations

Does not provide a built-in distributed locking mechanism; relies on the host file system or tmux for process coordination. Durable state requires external serialization logic.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Multi Agent Orchestration Master Library