team

Coordinate multiple AI agents on a shared task list via tmux sessions.

1|Updated Mar 15, 2026
One-click install
npx skills add https://github.com/Linnanli/xClaw --skill team-linnanli
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: team
Source: https://github.com/Linnanli/xClaw/tree/main/.codex/skills/team
Command: npx skills add https://github.com/Linnanli/xClaw --skill team-linnanli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Team-based orchestration to coordinate multiple AI agents on a shared task list using tmux-powered sessions, enabling durable parallel execution and collaborative workflows.

Core Features & Use Cases

  • tmux-based parallel execution for OMX with real worker Codex and Claude sessions in split panes, enabling coordinated reasoning and workload distribution.
  • State-driven coordination via .omx/state/team/... files and omx team api for messaging, task assignment, and lifecycle management.
  • Robust operator workflow with explicit startup, progress tracking, and safe cleanup across long-running automation tasks.
  • Use Case: running coordinated analyses, multi-agent experiments, or parallel task execution that survives session interruptions.

Quick Start

Launch a coordinated team by invoking omx team with a task description and observe the worker panes for progress.

Frequently Asked Questions about team

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

FAQPage Schema
How do I coordinate multiple AI agents on a shared task list in parallel?

Coordinate multiple AI agents on a shared task list using tmux-powered sessions that enable durable parallel execution and collaborative workflows. Real worker sessions in split panes handle coordinated reasoning and workload distribution.

How does tmux-based orchestration manage state for long-running multi-agent workflows?

Tmux-based orchestration manages state for long-running multi-agent workflows via `.omx/state/team/...` files and the `omx team api` for messaging, task assignment, and lifecycle management across durable sessions.

Can I run Codex and Claude workers together in split tmux panes for collaborative automation?

You can run Codex and Claude workers together in split tmux panes for collaborative automation. This setup supports real worker sessions, enabling coordinated reasoning and parallel workload distribution.

What is the best way to ensure parallel AI task execution survives session interruptions?

The best way to ensure parallel AI task execution survives session interruptions is using tmux-based orchestration with `.omx` state files. This approach provides durable parallel execution and safe cleanup across long-running automation tasks.

When do I need OMX-driven workflows for AI agent coordination?

You need OMX-driven workflows for AI agent coordination when running coordinated analyses, multi-agent experiments, or parallel task execution that requires durable runtime orchestration and inter-agent messaging.

What are the limitations of using tmux pane coordination for multi-agent task dispatch?

Limitations of using tmux pane coordination for multi-agent task dispatch include relying on explicit startup, progress tracking, and safe cleanup procedures to maintain robust operator workflows across long-running automation tasks.