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    pi-orchestration

    Orchestrate multiple AI models (GLM, MiniMax, etc.)

    By @dbhurley
    View on GitHub
    SKILL.md
    ---
    name: pi-orchestration
    description: Orchestrate multiple AI models (GLM, MiniMax, etc.) as workers using Pi Coding Agent with Claude as coordinator.
    homepage: https://github.com/mariozechner/pi-coding-agent
    metadata: {"clawdis":{"emoji":"🎭","requires":{"bins":["pi"]}}}
    ---
    
    # Pi Orchestration
    
    Use Claude as an orchestrator to spawn and coordinate multiple AI model workers (GLM, MiniMax, etc.) via Pi Coding Agent.
    
    ## Supported Providers
    
    | Provider | Model | Status |
    |----------|-------|--------|
    | **GLM** | glm-4.7 | ✅ Working |
    | **MiniMax** | MiniMax-M2.1 | ✅ Working |
    | OpenAI | gpt-4o, etc. | ✅ Working |
    | Anthropic | claude-* | ✅ Working |
    
    ## Setup
    
    ### 1. GLM (Zhipu AI)
    
    Get API key from [open.bigmodel.cn](https://open.bigmodel.cn/)
    
    ```bash
    export GLM_API_KEY="your-glm-api-key"
    ```
    
    ### 2. MiniMax
    
    Get API key from [api.minimax.chat](https://api.minimax.chat/)
    
    ```bash
    export MINIMAX_API_KEY="your-minimax-api-key"
    export MINIMAX_GROUP_ID="your-group-id"  # Required for MiniMax
    ```
    
    ## Usage
    
    ### Direct Commands
    
    ```bash
    # GLM-4.7
    pi --provider glm --model glm-4.7 -p "Your task"
    
    # MiniMax M2.1
    pi --provider minimax --model MiniMax-M2.1 -p "Your task"
    
    # Test connectivity
    pi --provider glm --model glm-4.7 -p "Say hello"
    ```
    
    ### Orchestration Patterns
    
    Claude (Opus) can spawn these as background workers:
    
    #### Background Worker
    ```bash
    bash workdir:/tmp/task background:true command:"pi --provider glm --model glm-4.7 -p 'Build feature X'"
    ```
    
    #### Parallel Army (tmux)
    ```bash
    # Create worker sessions
    tmux new-session -d -s worker-1
    tmux new-session -d -s worker-2
    
    # Dispatch tasks
    tmux send-keys -t worker-1 "pi --provider glm --model glm-4.7 -p 'Task 1'" Enter
    tmux send-keys -t worker-2 "pi --provider minimax --model MiniMax-M2.1 -p 'Task 2'" Enter
    
    # Check progress
    tmux capture-pane -t worker-1 -p
    tmux capture-pane -t worker-2 -p
    ```
    
    #### Map-Reduce Pattern
    ```bash
    # Map: Distribute subtasks to workers
    for i in 1 2 3; do
      tmux send-keys -t worker-$i "pi --provider glm --model glm-4.7 -p 'Process chunk $i'" Enter
    done
    
    # Reduce: Collect and combine results
    for i in 1 2 3; do
      tmux capture-pane -t worker-$i -p >> /tmp/results.txt
    done
    ```
    
    ## Orchestration Script
    
    ```bash
    # Quick orchestration helper
    uv run {baseDir}/scripts/orchestrate.py spawn --provider glm --model glm-4.7 --task "Build a REST API"
    uv run {baseDir}/scripts/orchestrate.py status
    uv run {baseDir}/scripts/orchestrate.py collect
    ```
    
    ## Best Practices
    
    1. **Task Decomposition**: Break large tasks into independent subtasks
    2. **Model Selection**: Use GLM for Chinese content, MiniMax for creative tasks
    3. **Error Handling**: Check worker status before collecting results
    4. **Resource Management**: Clean up tmux sessions after completion
    
    ## Example: Parallel Code Review
    
    ```bash
    # Claude orchestrates 3 workers to review different files
    tmux send-keys -t worker-1 "pi --provider glm -p 'Review auth.py for security issues'" Enter
    tmux send-keys -t worker-2 "pi --provider minimax -p 'Review api.py for performance'" Enter  
    tmux send-keys -t worker-3 "pi --provider glm -p 'Review db.py for SQL injection'" Enter
    
    # Wait and collect
    sleep 30
    for i in 1 2 3; do
      echo "=== Worker $i ===" >> review.md
      tmux capture-pane -t worker-$i -p >> review.md
    done
    ```
    
    ## Notes
    
    - Pi Coding Agent must be installed: `npm install -g @anthropic/pi-coding-agent`
    - GLM and MiniMax have generous free tiers
    - Claude acts as coordinator, workers do the heavy lifting
    - Combine with process tool for background task management