
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)
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| Installs | 1.4k |
|---|---|
| repo stars | ★ 238k |
| Last updated | August 5, 2026 |
| Repository | affaan-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
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
nto create a new pane with a prompt - Press
mto 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 backWorkflow 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 contextPattern 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 panePattern 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 reportBest 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/billingComplementary Tools
| Tool | What It Does | When to Use |
|---|---|---|
| dmux | tmux pane management for agents | Parallel agent sessions |
| Superset | Terminal IDE for 10+ parallel agents | Large-scale orchestration |
| Claude Code Task tool | In-process subagent spawning | Programmatic parallelism within a session |
| Codex multi-agent | Built-in agent roles | Codex-specific parallel work |
Troubleshooting
- Pane not responding: Check if the agent session is waiting for input. Use
mto 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) orapt install tmux(Linux).
interface:
display_name: "dmux Workflows"
short_description: "Multi-agent orchestration with dmux"
brand_color: "#14B8A6"
default_prompt: "Use $dmux-workflows to orchestrate parallel agent sessions with dmux."
policy:
allow_implicit_invocation: true
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.