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Dmux Workflows

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affaan-m
dmux-workflows

Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.

Overview

Publisheraffaan-m
RepositoryECC
Skill namedmux-workflows
Stars
270.2K
Forks
40.4K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Dmux Workflows AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/affaan-m/ECC/tree/main/.agents/skills/dmux-workflows
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/dmux-workflows .claude/skills/dmux-workflows
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dmux Workflows in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dmux Workflows on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dmux Workflows is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
# 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:

bash
# 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).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dmux Workflows AI skill do?

Multi-agent orchestration using dmux (tmux pane manager for AI agents). Patterns for parallel agent workflows across Claude Code, Codex, OpenCode, and other harnesses. Use when running multiple agent sessions in parallel or coordinating multi-agent development workflows.

Why use Dmux Workflows on TypingMind?

Because you install it once and use it with any model. Dmux Workflows is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dmux Workflows in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/dmux-workflows. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dmux Workflows?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dmux Workflows?

As many as you like. As long as a model supports skills, you can use Dmux Workflows with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dmux Workflows AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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