dmux-workflows

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• SKILL.md
• openai.yaml · agents/

SKILL.md (first lines)

# 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 OpenAI Codex, 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: OpenAI Codex, Codex, OpenCode, Cline, Gemini, Qwen

**Install:** `npm install -g dmux` or see [github.com/standardagents/dmux](https://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 (OpenAI Codex): "Review the security of the auth module"
Pane 2 (Codex): "Refactor the utility functions for performance"
Pane 3 (OpenAI Codex): "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 && codex
# Pane 2: cd ../feature-billing && codex

# Merge branches when done

From mturac/everything-openai-codex, MIT.

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