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dmux Workflows

Free

Orchestrate parallel AI agent sessions with ease.

by affaan-m239.3k stars on affaan-m/ecc
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Updated Aug 10, 2026
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Free · Opens the source repo

What dmux Workflows does

dmux Workflows is a powerful tool designed for orchestrating multiple AI agent sessions in parallel using tmux, a terminal multiplexer. This skill is particularly useful for developers and designers who need to manage complex workflows that involve multiple tasks or agents simultaneously. By leveraging dmux, users can create separate panes for different agent sessions, allowing for efficient multitasking and collaboration across various AI tools like Claude Code, Codex, and OpenCode.

The core functionality of dmux revolves around its ability to manage agent panes effectively. Users can create new panes with a simple command, input their prompts, and let each agent work independently. Once the tasks are completed, results can be merged back into the main session, streamlining the workflow and enhancing productivity. This capability is especially beneficial for tasks that can be broken down into independent components, such as research and implementation, multi-file features, or testing and fixing loops.

With predefined patterns for common workflows, dmux makes it easy to set up and execute complex tasks. For instance, users can run a research task in one pane while implementing code in another, or they can set up a code review process that simultaneously checks for security, performance, and coverage issues. This flexibility allows teams to take advantage of parallelism, reducing the overall time needed to complete projects.

Overall, dmux Workflows is an essential skill for anyone looking to optimize their AI agent interactions and enhance their development processes. Whether you're coordinating multi-agent development workflows or simply running multiple sessions, dmux provides the tools necessary to manage your tasks effectively and efficiently.

When to use it

Use dmux Workflows when you need to run multiple AI agent tasks in parallel or coordinate complex workflows across different agents.

When not to use it

This tool is not suitable for tasks that require sequential dependencies between agent outputs, as it is designed for independent parallel tasks.

What you can build with it

Research and Implementation

Run a research task in one pane while implementing code in another, merging findings once research is complete.

Multi-File Development

Parallelize work across different files, such as creating schemas, building APIs, and developing UI components.

Testing and Debugging

Set up one pane to run tests in watch mode while using another pane to fix any failing tests based on the output.

How to install dmux Workflows

View source

1. Install with the skills CLI

npx skills add affaan-m/ecc/dmux-workflows --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by affaan-m

dmux Workflows

Orchestrate parallel AI agent sessions using dmux, a tmux pane manager for agent harnesses.

When to Activate

  • Running multiple agent sessions in parallel
  • Coordinating work across Claude Code, Codex, and other harnesses
  • Complex tasks that benefit from divide-and-conquer parallelism
  • User says "run in parallel", "split this work", "use dmux", or "multi-agent"

What is dmux

dmux is a tmux-based orchestration tool that manages AI agent panes:

  • Press n to create a new pane with a prompt
  • Press m to merge pane output back to the main session
  • Supports: Claude Code, Codex, OpenCode, Cline, Gemini, Qwen

Install: npm install -g dmux or see github.com/standardagents/dmux

Quick Start

# Start dmux session
dmux

# Create agent panes (press 'n' in dmux, then type prompt)
# Pane 1: "Implement the auth middleware in src/auth/"
# Pane 2: "Write tests for the user service"
# Pane 3: "Update API documentation"

# Each pane runs its own agent session
# Press 'm' to merge results back

Workflow Patterns

Pattern 1: Research + Implement

Split research and implementation into parallel tracks:

Pane 1 (Research): "Research best practices for rate limiting in Node.js.
  Check current libraries, compare approaches, and write findings to
  /tmp/rate-limit-research.md"

Pane 2 (Implement): "Implement rate limiting middleware for our Express API.
  Start with a basic token bucket, we'll refine after research completes."

# After Pane 1 completes, merge findings into Pane 2's context

Pattern 2: Multi-File Feature

Parallelize work across independent files:

Pane 1: "Create the database schema and migrations for the billing feature"
Pane 2: "Build the billing API endpoints in src/api/billing/"
Pane 3: "Create the billing dashboard UI components"

# Merge all, then do integration in main pane

Pattern 3: Test + Fix Loop

Run tests in one pane, fix in another:

Pane 1 (Watcher): "Run the test suite in watch mode. When tests fail,
  summarize the failures."

Pane 2 (Fixer): "Fix failing tests based on the error output from pane 1"

Pattern 4: Cross-Harness

Use different AI tools for different tasks:

Pane 1 (Claude Code): "Review the security of the auth module"
Pane 2 (Codex): "Refactor the utility functions for performance"
Pane 3 (Claude Code): "Write E2E tests for the checkout flow"

Pattern 5: Code Review Pipeline

Parallel review perspectives:

Pane 1: "Review src/api/ for security vulnerabilities"
Pane 2: "Review src/api/ for performance issues"
Pane 3: "Review src/api/ for test coverage gaps"

# Merge all reviews into a single report

Best Practices

  1. Independent tasks only. Don't parallelize tasks that depend on each other's output.
  2. Clear boundaries. Each pane should work on distinct files or concerns.
  3. Merge strategically. Review pane output before merging to avoid conflicts.
  4. Use git worktrees. For file-conflict-prone work, use separate worktrees per pane.
  5. Resource awareness. Each pane uses API tokens — keep total panes under 5-6.

Git Worktree Integration

For tasks that touch overlapping files:

# Create worktrees for isolation
git worktree add ../feature-auth feat/auth
git worktree add ../feature-billing feat/billing

# Run agents in separate worktrees
# Pane 1: cd ../feature-auth && claude
# Pane 2: cd ../feature-billing && claude

# Merge branches when done
git merge feat/auth
git merge feat/billing

Complementary Tools

ToolWhat It DoesWhen to Use
dmuxtmux pane management for agentsParallel agent sessions
SupersetTerminal IDE for 10+ parallel agentsLarge-scale orchestration
Claude Code Task toolIn-process subagent spawningProgrammatic parallelism within a session
Codex multi-agentBuilt-in agent rolesCodex-specific parallel work

Troubleshooting

  • Pane not responding: Check if the agent session is waiting for input. Use m to read output.
  • Merge conflicts: Use git worktrees to isolate file changes per pane.
  • High token usage: Reduce number of parallel panes. Each pane is a full agent session.
  • tmux not found: Install with brew install tmux (macOS) or apt install tmux (Linux).

Frequently asked questions about dmux Workflows

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