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Install in OpenClaw
/install gh-taskqueue
Description
In-memory priority task queue for AI agents. Create tasks with priorities and tags, claim the next highest-priority task, mark tasks complete or failed, filt...
README (SKILL.md)
TaskQueue
Priority task queue for agent workflows.
Start the server
uvicorn taskqueue.app:app --port 8014
Create a task
curl -s -X POST http://localhost:8014/v1/tasks \
-H "Content-Type: application/json" \
-d '{"title": "Scan skill", "payload": {"skill": "my-skill"}, "priority": 5, "tags": ["security"]}' | jq
Claim the next task (highest priority first)
curl -s -X POST http://localhost:8014/v1/claim | jq
Returns the task with status changed to running.
Complete or fail a task
curl -s -X POST http://localhost:8014/v1/tasks/TASK_ID/complete \
-H "Content-Type: application/json" \
-d '{"result": {"output": "all clear"}}' | jq
curl -s -X POST http://localhost:8014/v1/tasks/TASK_ID/fail \
-H "Content-Type: application/json" \
-d '{"error": "scan timed out"}' | jq
List and filter tasks
curl -s "http://localhost:8014/v1/tasks?status=pending" | jq
curl -s "http://localhost:8014/v1/tasks?tag=security" | jq
Queue stats
curl -s http://localhost:8014/v1/stats | jq
Returns total, pending, running, completed, failed.
Endpoints
| Method | Path | Description |
|---|---|---|
| POST | /v1/tasks | Create a task |
| GET | /v1/tasks | List tasks (filter: ?status=, ?tag=) |
| GET | /v1/tasks/{id} | Get task by ID |
| POST | /v1/claim | Claim next pending task |
| POST | /v1/tasks/{id}/complete | Mark done with result |
| POST | /v1/tasks/{id}/fail | Mark failed with error |
| GET | /v1/stats | Queue statistics |
Usage Guidance
This skill appears to do what it says (an in-memory FastAPI-based task queue). Before installing/running it, consider: (1) The HTTP API has no authentication—only run it on localhost or behind a firewall in a trusted environment; do not expose port 8014 to untrusted networks. (2) It is in-memory only: tasks are ephemeral and lost on process restart. (3) Installation uses common PyPI packages—use a virtual environment and pin package versions if you need reproducible/controlled installs. (4) If you will store any secrets in task payloads, avoid doing so because any process that can reach the server can read them. If you need authenticated or persistent behavior, prefer a queue implementation that supports those features.
Capability Analysis
Type: OpenClaw Skill
Name: gh-taskqueue
Version: 1.0.0
The gh-taskqueue skill is a standard FastAPI-based in-memory task management system. The code in taskqueue/app.py and taskqueue/state.py implements basic CRUD operations for tasks with priority and status tracking, using only legitimate dependencies (fastapi, uvicorn, pydantic) and no risky system or network interactions.
Capability Assessment
Purpose & Capability
Name/description (in-memory priority task queue) align with the included files and required binaries. The code implements task creation, claiming, completion/failure, listing, and stats as documented; required binary is only python and declared pip packages (fastapi/uvicorn/pydantic) are consistent with a FastAPI service.
Instruction Scope
SKILL.md simply instructs running uvicorn on port 8014 and using local HTTP curl endpoints, which matches the code. However, there is no authentication/authorization in the API—endpoints are unauthenticated HTTP—so if the server is reachable from other hosts, anyone who can connect can create/claim/modify tasks.
Install Mechanism
Install metadata indicates pip packages fastapi, uvicorn, pydantic (via the 'uv' pip spec). This is a standard package install from PyPI and proportionate to the service; no downloads from arbitrary URLs or archive extracts are present.
Credentials
The skill requests no environment variables, credentials, or config paths. The code does not access environment variables or external secrets, so requested environment access is minimal and proportional.
Persistence & Privilege
The skill does not request elevated system presence (always:false) and does not change other skills or system configs. Note: running the server opens a network port (8014) with no auth; that is a runtime exposure (not a declared privilege) and should be considered when deciding where to run it.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install gh-taskqueue - After installation, invoke the skill by name or use
/gh-taskqueue - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of gh-taskqueue: an in-memory priority task queue for AI agent workflows.
- Supports creating tasks with priorities and tags.
- Allows claiming the highest-priority pending task.
- Tasks can be marked as complete or failed.
- Filter tasks by status or tag and retrieve queue statistics.
- Lightweight API endpoints provided for easy integration.
Metadata
Frequently Asked Questions
What is Taskqueue?
In-memory priority task queue for AI agents. Create tasks with priorities and tags, claim the next highest-priority task, mark tasks complete or failed, filt... It is an AI Agent Skill for Claude Code / OpenClaw, with 90 downloads so far.
How do I install Taskqueue?
Run "/install gh-taskqueue" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Taskqueue free?
Yes, Taskqueue is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Taskqueue support?
Taskqueue is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Taskqueue?
It is built and maintained by mirni (@mirni); the current version is v1.0.0.
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