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dmux-workflows

Orchestrate parallel AI agent sessions across multiple harnesses using dmux.

Updated Aug 27, 2026
Search Tags:#multi-agent#orchestration#tmux#workflow-patterns#parallel-workflows#dmux#agent-harnesses
One-click install
npx skills add https://github.com/zh667/person-blog --skill dmux-workflows-zh667
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dmux-workflows
Source: https://github.com/zh667/person-blog/tree/main/.cursor/.agents/skills/dmux-workflows
Command: npx skills add https://github.com/zh667/person-blog --skill dmux-workflows-zh667

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrate parallel AI agent sessions across multiple harnesses using dmux, a tmux-based pane manager for AI agent workflows.

Core Features & Use Cases

  • Orchestrates multiple agent panes to run tasks in parallel across Claude Code, Codex, OpenCode, and other harnesses.
  • Provides quick-start patterns such as Research + Implement, Multi-File Feature, and Cross-Harness coordination to accelerate multi-agent development.
  • Offers straightforward pane management with creation, merging, and synchronized outputs to synthesize results.

Quick Start

Start a dmux session and begin creating agent panes to run and manage parallel agent tasks.

Frequently Asked Questions about dmux-workflows

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

FAQPage Schema
How do I run parallel AI agent sessions in tmux?▼

You can run parallel AI agent sessions by using dmux to orchestrate multiple harnesses across tmux panes. It manages pane creation, merging, and cross-pane data exchange to support scalable multi-agent development workflows.

Can I coordinate Claude Code and Codex agents in the same workflow?▼

Yes, dmux coordinates parallel tasks across Claude Code, Codex, OpenCode, and other harnesses. It enables cross-harness coordination by managing synchronized outputs and synthesizing results across multiple agent panes.

What are common multi-agent workflow patterns for parallel development?▼

Common multi-agent workflow patterns include Research + Implement, Multi-File Feature, and Cross-Harness coordination. These quick-start patterns accelerate development by orchestrating agent panes to run tasks in parallel.

How do I merge outputs from multiple AI agent panes?▼

You merge outputs from multiple AI agent panes using dmux's pane management features. It provides synchronized outputs and merging capabilities to synthesize results from parallel agent tasks across different harnesses.

Do I need tmux to manage parallel AI coding agents?▼

Yes, tmux is required as dmux enforces a tmux-based pane management approach. This architecture handles pane creation, merging, and cross-pane data exchange to support scalable multi-agent workflows.

How does cross-pane data exchange work in multi-agent orchestration?▼

Cross-pane data exchange in multi-agent orchestration works through dmux's tmux-based pane manager. It coordinates data sharing between parallel agent sessions to synthesize results and support scalable workflows across multiple harnesses.

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