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Dmux Workflows

  • 1.4k installs
  • 238k repo stars
  • Updated August 5, 2026
  • affaan-m/ecc

This is a copy of dmux-workflows by affaan-m - installs and ranking accrue to the original listing.

dmux-workflows is an agent orchestration skill that runs parallel AI coding sessions in tmux panes via dmux for developers who need divide-and-conquer multi-agent development across Claude Code, Codex, and OpenCode.

About

dmux-workflows is an affaan-m/ecc skill for multi-agent orchestration using dmux, a tmux pane manager built for AI agent harnesses. It defines patterns to run Claude Code, Codex, OpenCode, and other agents in parallel panes, split large tasks, and merge results from concurrent sessions. Activate when a user says run in parallel, split this work, use dmux, or coordinate multi-agent development on a complex refactor or feature slice. The skill covers pane layout, work partitioning, and cross-harness coordination rather than single-threaded prompting. Developers with tmux-based terminals and multiple agent CLIs gain the most from these workflows.

  • Orchestrates parallel AI agent sessions using tmux-based dmux pane manager
  • Supports Claude Code, Codex, OpenCode, Cline, Gemini, and Qwen harnesses
  • Keyboard shortcuts: press 'n' to spawn new agent pane, 'm' to merge output
  • Enables divide-and-conquer parallelism for complex development work
  • Activates on triggers like "run in parallel", "split this work", or "use dmux"

Dmux Workflows by the numbers

  • 1,373 all-time installs (skills.sh)
  • +90 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/affaan-m/ecc --skill dmux-workflows

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Listed on Skillselion
Installs1.4k
repo stars238k
Last updatedAugust 5, 2026
Repositoryaffaan-m/ecc

How do you run parallel AI agents in tmux?

Orchestrate multiple AI coding agents running in parallel panes so they can divide complex tasks and merge results automatically.

Who is it for?

Developers using tmux who run Claude Code, Codex, or OpenCode and need parallel pane orchestration for large divide-and-conquer coding tasks.

Skip if: Single-agent serial workflows, GUI-only terminals without tmux, or teams unwilling to manage multiple concurrent agent sessions.

When should I use this skill?

User says run in parallel, split this work, use dmux, multi-agent, or coordinate Claude Code and Codex sessions simultaneously.

What you get

Parallel dmux tmux pane layout, partitioned agent task assignments, and merged multi-agent development results.

  • Parallel pane layout plan
  • Partitioned task assignments
  • Merged agent output

Files

SKILL.mdMarkdownGitHub ↗

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).

Related skills

How it compares

Use dmux-workflows for runtime tmux pane parallelism; use team-builder when you only need to select persona agents before any panes are launched.

FAQ

What is dmux in dmux-workflows?

dmux-workflows uses dmux as a tmux pane manager purpose-built for AI agent harnesses, letting developers run Claude Code, Codex, OpenCode, and similar CLIs side by side in parallel panes on one machine.

When should dmux-workflows activate?

dmux-workflows activates when tasks benefit from divide-and-conquer parallelism—large refactors, multi-module features, or explicit user requests to run agents in parallel, split work, or use dmux.

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