A comprehensive framework for spawning, managing, and tracking AI sub-agents through structured prompt engineering and systematic execution.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install abel-agent-orchestration
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 abel-agent-orchestration 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 Orchestration?
Agent Orchestration is a professional-grade skill designed to solve the common pitfalls of vague AI prompting. By reframing a prompt as a technical contract rather than a simple request, this skill enables developers to command AI models with architectural precision. It is part of the Hal Stack and focuses on eliminating hallucinations by providing models with clear identities, labeled context, and explicit process instructions.
Using this skill within the ecosystem of Openclaw Skills allows you to move beyond basic chat interactions into sophisticated agent management. It provides the tools to direct how an answer is formed, ensuring that the model follows a logical sequence of operations before delivering a final, structured result.
Agent Orchestration Use Cases
- Orchestrating complex software builds that require multiple specialized sub-agents.
- Conducting deep market research that requires fact-checking and structured data extraction.
- Automating code reviews and technical writing with consistent expert personas.
- Managing high-volume, repetitive tasks with optimized cost routing via the SkillBoss API Hub.
How Agent Orchestration Works
- Identity Layer: Assign a specific professional role and expertise level to the model.
- Context Layer: Provide ordered, scoped, and labeled information distinguishing rules from history.
- Task Layer: Define a precise, measurable objective for the agent to accomplish.
- Process Layer: Direct the step-by-step logic the model must follow to form its output.
- Output Layer: Specify the exact format, such as JSON or Markdown, for the final deliverable.
- Execution: Route the request through the SkillBoss API Hub with the appropriate preference setting (quality, balanced, or price).
Agent Orchestration Setup
To begin using this skill, you must configure your environment with the necessary API credentials. Ensure you have access to the SkillBoss API Hub.
export SKILLBOSS_API_KEY="your_secret_key"
Follow the provided prompt templates to structure your agent requests, ensuring all five layers of the architecture are present in your system instructions.
Agent Orchestration Data Schema & Taxonomy
The Agent Orchestration skill utilizes a structured file system to track progress and refine performance. This ensures that every sub-agent spawned via Openclaw Skills is accountable and its outputs are documented.
| File Path | Purpose |
|---|---|
| notes/areas/active-agents.md | A live tracking file for running and completed sub-agent tasks. |
| LEARNINGS.md | A historical log of successful approaches and failed experiments. |
| docs/constraints.md | A canonical reference for rules that agents must never violate. |
| roles/library.md | A repository of reusable expert identity definitions. |
Agent Orchestration Advanced Features
- Ralph Mode: An autonomous retry logic that forces agents to debug and iterate until success criteria are met.
- Model Auto-Routing: Dynamic task allocation to the best model based on quality, balance, or price settings.
- Acceptance Loops: Integration of user stories to verify that agent outputs meet specific project requirements.
- Heartbeat Monitoring: Systematic tracking of agent sessions to prevent orphaned or stalled processes.
SKILL.md
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