Dmux workflows
Skill onfire7777/universal-ai-skills-library/skills/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.From its SKILL.md
npx -y skills add onfire7777/universal-ai-skills-library --skill dmux-workflowsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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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 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
- Independent tasks only. Don't parallelize tasks that depend on each other's output.
- Clear boundaries. Each pane should work on distinct files or concerns.
- Merge strategically. Review pane output before merging to avoid conflicts.
- Use git worktrees. For file-conflict-prone work, use separate worktrees per pane.
- 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
| 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).
What ships with it: 1 file
260 B alongside SKILL.md
agents/
- openai.yaml260 B
Gives 1 of the 12 instructions most agent orchestration skills give in ~1.1k tokens
Counted across 848 of the 1,300 authors here whose files we hold, read 2026-09-06
- Dispatch one agent per independent problem domainin 56 of 848, across 42 files
- Run full test suite after integrationin 55 of 848, across 42 files
- Verify fixes do not conflictin 40 of 848, across 32 files
- Review each summary when agents returnin 40 of 848, across 31 files
- Write a handoff document summarising the current conversationin 30 of 848, across 25 files
- Reference existing artifacts by path or URLin 26 of 848, across 24 files
- Give each agent a specific scopein 19 of 848, across 10 files
- Give each agent a clear goalin 19 of 848, across 10 files
- Include a suggested skills section in the documentin 18 of 848, across 16 files
- Tailor the doc to the user argumentsin 18 of 848, across 15 files
- Issue all subagent dispatches in the same responsein 17 of 848, across 11 files
- Use git worktrees for isolationhere, and in 17 of 848, across 8 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.