agent-orchestrator

What This Skill Does

The Agent Orchestrator is a powerful meta-agent framework designed to handle macro-level tasks by decomposing them into manageable, parallelizable subtasks. It automates the entire lifecycle of multi-agent collaboration, including workspace generation, task delegation, and result consolidation. By dynamically spawning sub-agents with custom SKILL.md configurations, it allows OpenClaw to solve complex, multi-faceted problems without manual oversight, ensuring clear communication protocols and structured output delivery via file-based inbox and outbox architectures.

Installation

To integrate the Agent Orchestrator into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/aatmaan1/agent-orchestrator Ensure your local environment is configured for sub-agent management and that the required Python dependency scripts are available in your path.

Use Cases

• Complex Software Development: Decompose a feature request into design, implementation, and testing sub-agents.
• Content Pipeline Automation: Manage a research agent, a writing agent, and an editorial agent simultaneously for long-form reports.
• Data Engineering: Spawn agents for data cleaning, transformation, and storage operations in parallel to optimize throughput.
• Cross-Platform Deployment: Orchestrate separate agents for documentation updates, code builds, and automated deployment verification.

Example Prompts

1. "Orchestrate a multi-agent task to research current AI regulations and decompose the findings into a summarized report and a draft policy document."
2. "I need to decompose the task of migrating our legacy database documentation; spawn parallel agents to parse, validate, and reformat our existing schema files."
3. "Use the agent factory to delegate the creation of a front-end UI prototype, assigning specific sub-agents to handle the CSS, React component structure, and unit tests independently."

Tips & Limitations

• Decomposition: The quality of the final output is highly dependent on the granularity of your decomposition phase. Aim for isolated tasks to prevent inter-agent dependency deadlocks.
• Monitoring: While the orchestrator is autonomous, monitor the status.json files periodically for complex, long-running tasks to ensure no sub-agent has stalled.
• Resource Usage: Spawning large numbers of sub-agents consumes system memory and CPU. Limit concurrent agents based on your host environment's capacity.
• Validation: Always review consolidated outputs. While agents are specialized, cross-file consistency may require a final human or meta-agent verification pass.