A lightweight Python engine that orchestrates multi-agent squads by routing tasks to the most efficient model based on skill requirements and cost.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aoineco-squad-dispatch
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 aoineco-squad-dispatch 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 Aoineco Squad Dispatch?
Aoineco Squad Dispatch is a sophisticated orchestration engine designed to manage multi-agent squads with high precision. It solves the common challenge of resource wastage in AI workflows by ensuring that tasks are routed to the most appropriate agent based on their specific specialization and cost profile. By integrating this into the Openclaw Skills framework, developers can ensure that expensive models like Claude Opus are reserved for high-level strategy, while more economical models handle routine data processing or content generation.
This skill is built for the $7 Bootstrap Protocol, meaning it is highly optimized for performance with zero external dependencies. It provides a pre-configured roster of seven distinct agents—including specialists for security, research, and community management—allowing for a structured and scalable approach to multi-agent task management.
Aoineco Squad Dispatch Use Cases
- Orchestrating complex workflows that require specialized AI roles such as security auditing, content creation, and data research.
- Reducing operational costs by automatically routing simple tasks to cheaper models like Gemini Flash.
- Managing parallel task execution to significantly speed up project completion times.
- Creating visual, human-readable dispatch plans to monitor agent assignments and resource allocation.
How Aoineco Squad Dispatch Works
- The user defines a set of tasks, each with specific skill requirements, priority levels, and optional dependencies.
- The dispatch engine analyzes the task requirements and matches them against the specializations in the pre-configured agent roster.
- The system detects dependencies to determine which tasks must run sequentially and which can be executed in parallel.
- A visual dispatch plan is generated, providing a roadmap of agent assignments and estimated cost tiers.
- The tasks are routed through the Openclaw Skills ecosystem, ensuring the right agent handles the right job at the optimal price point.
Aoineco Squad Dispatch Setup
To deploy this skill in your environment, follow these steps:
# Ensure you have Python 3.10+ installed on your system
# Navigate to your project directory
# Clone or copy the dispatch_engine.py script into your scripts folder
# No pip installations are required as the engine uses pure Python
You can then import the SquadDispatcher into your Python applications to start managing your agent squad.
Aoineco Squad Dispatch Data Schema & Taxonomy
The engine organizes task and agent metadata to ensure efficient routing and transparency.
| Component | Description |
|---|---|
| Agent Roster | Maps agent names (e.g., Oracle, Blue-Blade) to specific specializations and cost tiers. |
| Task Registry | Tracks task IDs, required skills, and specific model preferences. |
| Dependency Map | Identifies relationships between tasks to manage execution order and parallelism. |
| Dispatch Plan | A structured output summarizing the assignment of tasks to agents and expected costs. |
Aoineco Squad Dispatch Advanced Features
- Skill-Based Routing: Matches task requirements to specific agent specializations automatically.
- Dependency Detection: Automatically identifies and groups tasks for parallel execution to save time.
- Cost-Aware Dispatching: Prioritizes the use of cheaper models for routine tasks while reserving top-tier models for critical needs.
- Load Balancing: Respects maximum concurrency limits for each agent to prevent performance bottlenecks.
SKILL.md
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