oh-my-claudecode: a multi-agent orchestration layer for Claude Code with automated staged pipelines and multi-model coordination

oh-my-claudecode: a multi-agent orchestration layer for Claude Code with automated staged pipelines and multi-model coordination

What it solves

Oh-my-claudecode (OMC) is a multi-agent orchestration layer for Claude Code. It removes the learning curve of using Claude Code by providing a natural language interface and automated workflows that distribute complex tasks across specialized agents, ensuring tasks are verified as complete rather than partially finished.

How it works

OMC integrates as a plugin for Claude Code or a standalone CLI. It uses several orchestration modes to handle different task complexities:

  • Team Mode: A staged pipeline (Plan $\rightarrow$ PRD $\rightarrow$ Execute $\rightarrow$ Verify $\rightarrow$ Fix) that coordinates multiple agents on a shared task list.
  • CLI Workers: Spawns real tmux panes to run other AI CLIs like Codex, Gemini, Antigravity, Grok, or Cursor-agent in parallel.
  • Autopilot: An autonomous single-lead agent for end-to-end feature work.
  • Specialized Agents: Employs 19 different agents tailored for architecture, research, design, and testing, routing tasks to the most efficient model (e.g., Haiku for simple tasks, Opus for complex reasoning).
  • Deep Interview: Uses Socratic questioning to clarify requirements before any code is written.

Who it’s for

Developers using Claude Code who want to automate complex software engineering workflows, leverage multiple AI models/CLIs simultaneously, and reduce token costs through smart routing.

Highlights

  • Multi-Model Orchestration: Ability to spawn and coordinate workers from different AI providers (Codex, Gemini, etc.) via tmux.
  • Zero Configuration: Works out of the box with intelligent defaults.
  • Staged Pipelines: Structured workflows that Team mode uses to ensure quality and verification.
  • Socratic Requirement Gathering: The /deep-interview skill clarifies vague ideas before execution.
  • Cost Optimization: Smart routing of tasks between models to save 30-50% on tokens.

Sources