pi-orchestration

What This Skill Does

The pi-orchestration skill empowers your OpenClaw AI agent to act as a sophisticated manager, delegating complex development tasks to a "parallel army" of specialized AI models. While Claude (Opus) maintains high-level logic and coordination, this skill allows the agent to spin up sub-processes powered by GLM-4.7, MiniMax-M2.1, and other providers to execute specific, compartmentalized workloads simultaneously. By leveraging the Pi Coding Agent, you can achieve true parallel processing, allowing for rapid code generation, comprehensive multi-file security audits, and complex data processing tasks that would otherwise require sequential execution.

Installation

To install this skill, use the ClawHub command within your terminal: clawhub install openclaw/skills/skills/dbhurley/pi-orchestration

Ensure your environment variables are configured correctly for the models you intend to use. For GLM, define GLM_API_KEY, and for MiniMax, define MINIMAX_API_KEY and MINIMAX_GROUP_ID in your shell configuration or .env file.

Use Cases

• Large Scale Codebase Audits: Distribute multiple files across different AI workers to perform concurrent security checks and performance analysis.
• Map-Reduce Task Processing: Automatically break down a large project into smaller, manageable chunks, assign each to a model worker, and compile the final outputs into a single consolidated report.
• Polyglot Development: Utilize specific models known for different strengths (e.g., GLM for Chinese language context vs. MiniMax for creative logic) to handle diverse sub-tasks within a single master project.

Example Prompts

1. "Orchestrate a parallel review of the authentication and database modules using GLM and MiniMax, then consolidate the findings into a report."
2. "Use the pi-orchestration skill to spawn three background workers to generate unit tests for the newly created REST API endpoints."
3. "Run a map-reduce operation to process these 10 documentation files in parallel and save the formatted output to a directory named /tmp/docs."

Tips & Limitations

• Task Decomposition: The effectiveness of this skill relies on your ability to break tasks into atomic units. If tasks have high interdependency, they may not be suitable for parallel execution.
• Resource Management: Since this skill utilizes tmux for background processes, always ensure you clean up sessions using a script or manual command to avoid orphaned processes consuming system memory.
• Model Strengths: Always pair the right model with the right task. Use the model's inherent strengths to improve the quality of output, such as using GLM for localized content generation.
• Monitoring: Always use the pi-orchestration status check commands periodically, especially during long-running tasks, to ensure workers have not hit rate limits or encountered unexpected execution errors.