A lightweight, in-memory priority task queue designed to manage complex AI agent workflows and task orchestration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install gh-taskqueue
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 gh-taskqueue 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 gh-taskqueue?
gh-taskqueue provides a robust backend for AI agents to manage their workload using an in-memory priority queue. Similar to a lightweight Celery for AI-driven environments, this Openclaw Skills integration allows developers to organize tasks with specific priorities and tags, ensuring that the most critical operations are handled first.
Built on FastAPI and Pydantic, the skill offers a clean REST API for creating, claiming, and tracking tasks through their entire lifecycle. It is particularly effective for multi-agent systems where task distribution and status monitoring are essential for maintaining operational efficiency and reliability.
gh-taskqueue Use Cases
- Orchestrating multi-step AI agent workflows that require sequential or prioritized execution.
- Managing security scans or data processing jobs where high-priority tasks must jump to the front of the line.
- Providing a centralized hub for multiple agents to claim and report on distributed work items.
- Real-time monitoring of agent productivity through task completion and failure statistics.
How gh-taskqueue Works
- The user or a parent agent initializes the server to act as a central task repository.
- Tasks are submitted via the POST /v1/tasks endpoint with assigned priority levels (0-10) and descriptive tags.
- Worker agents use the /v1/claim endpoint to automatically retrieve the highest-priority pending task.
- Once an agent completes the work, they update the task status to completed or failed with associated output or error logs.
- Developers monitor the entire pipeline using the statistics and filtering endpoints to ensure workflow health.
gh-taskqueue Setup
To get started with this Openclaw Skills component, ensure you have Python installed, then set up the environment:
# Install dependencies using uv
uv pip install fastapi uvicorn pydantic
# Start the task queue server
uvicorn taskqueue.app:app --port 8014
gh-taskqueue Data Schema & Taxonomy
The task queue organizes data using a structured JSON schema for each task, allowing for easy filtering and reporting:
| Field | Type | Description |
|---|---|---|
| id | String | Unique UUID for the task |
| title | String | Human-readable name of the task |
| payload | Object | Custom data required for task execution |
| priority | Integer | Priority level (higher numbers processed first) |
| status | String | Current state: pending, running, completed, or failed |
| tags | Array | Metadata labels for category-based filtering |
| result/error | Object | The final output or error message upon completion |
gh-taskqueue Advanced Features
- Smart Priority Claiming: Automatically selects the most important task available to prevent bottlenecking.
- Flexible Tagging System: Allows for granular filtering of tasks by specific domains like security, frontend, or data-science.
- In-Memory Performance: Low-latency operations suitable for fast-moving AI agent interactions.
- Real-time Stats: Provides instant visibility into the total, pending, running, and failed task counts to optimize agent scaling.
SKILL.md
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