Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A meta-agent skill that decomposes complex tasks into parallelizable subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, and consolidates their outputs. Agents communicate through a file-based inbox/outbox protocol, with status.json tracking each agent's state from pending to completed or failed.
Parallel sub-agent execution cuts total task time compared to sequential single-agent workflows, particularly when subtasks have no shared dependencies.
When to use it
- Running parallel research agents across multiple independent topics
- Breaking a large codebase refactor into isolated agent-owned modules
- Generating multi-section reports with dedicated collector, analyst, writer, and reviewer agents
- Coordinating sequential data pipeline stages where one agent feeds the next
- Delegating documentation tasks across specialized agents with automatic output consolidation
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Create a comprehensive market analysis report
- 1Decompose the task into four subtasks: data collection, analysis, writing, and review, with an explicit dependency chain
- 2Generate a workspace and SKILL.md for each sub-agent using scripts/create_agent.py
- 3Write task instructions to each agent's inbox/ directory and set status.json to pending
- 4Spawn agents via the Task tool and poll status.json files until all report completed
- 5Collect outputs from each outbox/, merge sections, and run scripts/dissolve_agents.py to archive workspaces
A consolidated market analysis report with a summary of each agent's deliverables and any issues encountered