A powerful multi-agent orchestration tool that implements five proven patterns to coordinate multiple AI agents for complex task resolution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-orchestrator-molter-102
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 agent-orchestrator-molter-102 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 Agent Orchestrator?
Agent Orchestrator is a sophisticated framework designed to manage multi-agent workflows within the OpenClaw environment. It moves beyond simple prompt-response interactions by implementing structural patterns like Work Crew, Supervisor, and Expert Council. By utilizing these Openclaw Skills, developers can parallelize research, automate specialist routing, and ensure high-confidence outputs through redundant verification and synthesis.
This skill is particularly valuable for high-stakes decision-making and intricate technical workflows where a single agent's context window or reasoning capability might be insufficient. While it incurs higher token costs, the trade-off is a significant increase in the quality and breadth of the generated results, making it a cornerstone for professional AI automation.
Agent Orchestrator Use Cases
- Executing parallel research tasks to gain diverse perspectives on complex topics like market trends or security risks.
- Implementing multi-stage content pipelines that handle drafting, editing, and final review automatically.
- Performing rigorous code audits or smart contract reviews using redundancy and best-of-N verification.
- Automatically routing diverse incoming tasks to the most qualified specialists like researchers, coders, or analysts.
How Agent Orchestrator Works
- The user initiates a specific orchestration pattern command such as crew, supervise, or route via the CLI.
- The system analyzes the task requirements and, if using the route pattern, classifies the task type against specialist profiles.
- Based on the selected pattern, the orchestrator spawns multiple sub-sessions asynchronously to handle different aspects of the task.
- Sub-agents execute their specific assignments, providing specialized input or completing sequential stages of a pipeline.
- The orchestrator aggregates, synthesizes, or filters the sub-agent outputs according to defined convergence strategies like consensus or best-of-selection.
- A final, consolidated result is returned to the user, providing a comprehensive solution derived from multiple expert perspectives.
Agent Orchestrator Setup
To begin using these Openclaw Skills for orchestration, ensure you are running OpenClaw 0.8 or higher. Install the skill and verify the configuration with the following commands:
# Verify OpenClaw version
claw --version
# Test the auto-routing capability to ensure specialists are available
claw agent-orchestrator route --task "Analyze this project structure" --specialists "coder,planner"
# Run a basic research crew to test parallelization
claw agent-orchestrator crew --task "Future of AI agents" --agents 2
Agent Orchestrator Data Schema & Taxonomy
The Agent Orchestrator manages data through structured session logs and synthesized outputs. The following table describes the primary data objects generated:
| Object | Description |
|---|---|
| Specialist Pool | A registry of available personas such as coder, researcher, and analyst. |
| Pattern Metadata | Configuration for convergence (consensus, best-of) and confidence thresholds. |
| Aggregated Result | The final consolidated markdown or JSON output from all sub-agents. |
| Session History | Traceable logs of every spawned sub-session for auditing and debugging. |
Agent Orchestrator Advanced Features
- Expert Council mode for multi-round deliberation and consensus-building on sensitive decisions.
- Adaptive Supervisor strategy for dynamic task decomposition when the workflow cannot be predefined.
- Intelligent routing with confidence thresholds that prompt for user confirmation on low-confidence classifications.
- Integration with OpenClaw session_spawn capabilities for massive asynchronous parallelization.
SKILL.md
Loading
Related Openclaw Skills
Agent Browser
A high-performance Rust-based headless browser CLI that allows AI agents to navigate, interact with, and extract data from web pages using structured commands.
Senior Brand Strategist
The Senior Brand Strategist skill automates high-level branding workflows and asset generation within the Openclaw Skills ecosystem.
TaoStats Skill
An advanced integration for the TaoStats API providing real-time Bittensor blockchain metrics, subnet economics, and validator performance data.
Proactive Agent
A skill that transforms passive AI agents into anticipatory assistants that take initiative, predict user needs, and provide proactive status updates.
Agent Orchestrator
A sophisticated multi-agent orchestration framework that implements five proven patterns for coordinating specialized AI agents to solve complex problems.
Deterministic Controller
A documentation-first template suite for building repeatable, evidence-gated orchestration loops in Openclaw Skills environments.
Featured*
