What problem does it solve? Coordinating multiple AI agent sessions manually is error-prone and slow when a task can be split into independent tracks. This Skill provides proven patterns for running parallel agent workflows with dmux, a tmux pane manager for AI agent harnesses. ## Core Features & Use Cases - Parallel Agent Orchestration: Create and manage multiple agent panes across Claude Code, Codex, OpenCode, Cline, Gemini, and Qwen from a single dmux session. - Workflow Patterns: Apply five ready-made patterns including research-plus-implement, multi-file feature splits, test-and-fix loops, cross-harness delegation, and parallel code review pipelines. - Git Worktree Isolation: Prevent merge conflicts by running agents in separate git worktrees when tasks touch overlapping files. - Use Case: Split a billing feature into three panes — one for database migrations, one for API endpoints, and one for UI components — then merge the results and integrate in the main pane. ## Quick Start Ask the agent to split the current task into independent subtasks and run them in parallel dmux panes, then merge the results back into the main session.