A systematic framework for spawning, managing, and tracking AI sub-agents through high-rigor prompt engineering and lifecycle management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 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 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 technical framework designed to move beyond conversational AI interactions toward engineered systems. It treats every prompt as a binding contract between the user and the agent, utilizing a 5-layer architecture—Identity, Context, Task, Process, and Output—to eliminate the ambiguity that leads to model hallucinations. By implementing this skill within Openclaw Skills, developers can ensure that their AI agents act as specialized experts with clear boundaries and measurable objectives.
This skill is part of the Hal Stack, focusing on the rigorous organization of agent lifecycles. It shifts the focus from simple output generation to the engineering of the reasoning process itself. By defining how an answer is formed rather than just what the answer should be, it enables the creation of complex, multi-step agent workflows that are both reliable and reproducible.
Agent Orchestration Use Cases
- Spawning specialized sub-agents for technical research, code review, or marketing positioning.
- Managing multiple concurrent agent sessions without losing track of process status.
- Reducing model hallucinations in complex tasks by providing structured constraints.
- Creating a reusable library of expert personas for different development phases.
- Implementing a feedback loop to improve prompt engineering based on historical agent performance.
How Agent Orchestration Works
- Define the Agent Identity to anchor the model to a specific expertise and behavioral pattern.
- Provide Labeled Context that separates immutable rules from editable state and historical data.
- Specify the Task and the internal Process steps the agent must execute, including reasoning requirements.
- Define Output Formats and Constraints to ensure the delivery meets exact technical specifications.
- Log the spawned agent in a tracking file to monitor runtime, status, and expected completion.
- Execute a verification loop against defined user stories before the agent reports completion.
- Update a centralized learnings database with the outcome to optimize future Openclaw Skills deployments.
Agent Orchestration Setup
To begin using Agent Orchestration, you must establish the necessary directory structure for tracking and learnings. Run the following commands in your workspace:
mkdir -p notes/areas
touch notes/areas/active-agents.md
touch LEARNINGS.md
Once created, ensure your agent configurations reference these files to maintain a real-time ledger of all active sub-agent processes.
Agent Orchestration Data Schema & Taxonomy
The skill utilizes a structured file-based taxonomy to manage agent data and metadata:
| File Path | Purpose | Key Metadata Fields |
|---|---|---|
notes/areas/active-agents.md |
Real-time agent tracking | Label, Task, Spawn Time, Status (Running/Stalled) |
LEARNINGS.md |
Continuous improvement log | Approach, Outcome, Lesson, Agent Label |
ROLE_LIBRARY.md |
Expert persona definitions | Role Name, Expertise, Style Traits, Behavioral Rules |
CONSTRAINTS.md |
Project-wide guardrails | Design Rules, API Limits, Voice/Tone Guidelines |
Agent Orchestration Advanced Features
- Ralph Mode: An advanced persistence logic that instructs agents to debug and iterate through failures rather than escalating immediately.
- Model-Specific Optimization: Custom prompt structures tailored for the unique reasoning profiles of Claude Opus, Sonnet, and GPT-4.
- Heartbeat Auditing: CLI-based synchronization between active system sessions and the tracking ledger to prevent orphan agents.
- Reasoning Layers: Requirements for agents to show their work at every step of the process to ensure logic transparency.
- User Story Acceptance Loops: Mandatory verification steps where agents must audit their own output against specific user goals.
SKILL.md
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