A production-ready orchestration system that manages parallel AI worker agents through a structured four-phase workflow for complex task execution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install multi-agent
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install multi-agent using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Multi-Agent Orchestration Skill?
The Multi-Agent Skill is a sophisticated orchestration framework designed for users of Openclaw Skills who need to scale their AI development workflows. It implements a robust Coordinator Mode that enables a primary agent to manage multiple parallel workers through a synchronized lifecycle. By leveraging real-world parallel execution, this skill transforms how complex tasks are handled, moving beyond single-agent limitations to a distributed model that ensures thorough research, precise implementation, and rigorous verification.
Built for high-reliability environments, the system utilizes a specialized XML-based communication protocol to ensure seamless data exchange between the coordinator and its spawned sessions. This approach allows Openclaw Skills to handle larger projects by breaking them down into manageable sub-tasks executed by specialized agents, all while maintaining a centralized state for persistence and recovery.
Multi-Agent Orchestration Skill Use Cases
- Scaling large-scale codebase refactoring across multiple directories simultaneously.
- Conducting deep-dive technical research from multiple perspectives before starting implementation.
- Automating a complete software development lifecycle from initial research to final regression testing.
- Managing complex migrations where parallel workers can handle isolated modules under a single coordinator.
How Multi-Agent Orchestration Skill Works
- The Coordinator analyzes the primary task and prepares worker specifications and dynamic prompts.
- Parallel Research Workers are spawned to explore different technical angles and surface dependencies.
- The Coordinator synthesizes research findings into a centralized Implementation Specification document.
- Implementation Workers are dispatched to execute code changes based on the approved synthesis.
- Verifier Workers perform final quality assurance, running tests and ensuring the integrity of the implementation.
Multi-Agent Orchestration Skill Setup
To begin using this orchestration system within your Openclaw Skills environment, follow these steps:
# Navigate to the multi-agent skill directory
cd skills/multi-agent
# Prepare a worker for a specific task
python3 scripts/coordinator_v2.py prepare "Task description" --role researcher
# Spawn the worker session using the generated prompt
prompt=$(cat .openclaw/scratchpad/prompts/prompt-{worker-id}.txt)
sessions_spawn --label "multi-agent-worker" --task "$prompt" --timeout 300
Multi-Agent Orchestration Skill Data Schema & Taxonomy
The skill maintains a structured scratchpad directory to manage shared knowledge and agent states within the Openclaw Skills ecosystem:
| Directory/File | Description |
|---|---|
| .openclaw/scratchpad/workers/ | Stores JSON-based state data for each active worker agent. |
| .openclaw/scratchpad/results/ | Contains the output findings and task notifications from completed workers. |
| .openclaw/scratchpad/specs/ | Houses the synthesized implementation specifications generated by the coordinator. |
| .openclaw/scratchpad/prompts/ | Stores the dynamically generated system prompts for new worker sessions. |
| coordinator_state.json | A global state file enabling breakpoint recovery and session tracking. |
Multi-Agent Orchestration Skill Advanced Features
- Parallel execution support via native sessions_spawn and sessions_send integration.
- Standardized XML protocol for structured task notifications and result reporting.
- Persistent state management allowing for the resumption of complex workflows after interruptions.
- Automated specification synthesis that converts raw research data into actionable developer docs.
- Built-in verification phase to enforce quality standards across all multi-agent Openclaw Skills tasks.
SKILL.md
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