tmux-workers

Spawn AI workers in separate tmux panes for parallel task monitoring.

4|Updated Jan 9, 2026
One-click install
npx skills add https://github.com/baleen37/bstack --skill tmux-workers
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tmux-workers
Source: https://github.com/baleen37/bstack/tree/main/plugins/me/skills/tmux-workers
Command: npx skills add https://github.com/baleen37/bstack --skill tmux-workers

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

tmux-workers solves the problem of monitoring and coordinating multiple AI agents by spawning separate tmux panes for each worker, enabling parallel task execution without blocking the main workflow.

Core Features & Use Cases

  • Parallel worker spawning: spawn claude, codex, and gemini in separate panes for concurrent task processing.
  • Real-time monitoring: view outputs live in split panes for quick feedback and debugging.
  • Deterministic layout: a defined pane arrangement keeps workers organized during complex workflows.

Quick Start

Start a tmux session and run tmux-workers to spawn claude, codex, and gemini in separate panes for parallel task execution.

Frequently Asked Questions about tmux-workers

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

FAQPage Schema
How do I run multiple AI workers in parallel using tmux?

To run parallel AI workers using tmux, you spawn separate tmux panes for each worker, enabling concurrent task execution without blocking the main workflow. This allows you to monitor outputs live for quick debugging.

Can I monitor multiple AI agents in real time during development workflows?

Yes, you can monitor multiple AI agents in real time by viewing their outputs live in split tmux panes. This provides quick feedback and keeps workers organized during complex experimentation workflows.

How do I spawn claude, codex, and gemini concurrently for parallel task processing?

You spawn claude, codex, and gemini concurrently by starting a tmux session and using a deterministic spawn pattern with unique IDs. This creates a defined pane arrangement for concurrent task processing across independent subtasks.

Do I need to manually clean up temporary task files after running parallel AI workers?

No, you do not need to manually clean up temporary task files after running parallel AI workers. The system automatically cleans up temporary task files after execution completes.

What is the best way to organize multiple AI agents in a single terminal window?

The best way to organize multiple AI agents in a single terminal window is using a deterministic tmux pane layout. This defined pane arrangement keeps workers organized during complex parallel workflows without blocking.

Does parallel AI worker spawning work for independent subtasks in experimentation workflows?

Yes, parallel AI worker spawning is specifically designed for running multiple AI workers across independent subtasks in development and experimentation workflows. It coordinates pane management through tmux to prevent blocking.