Host Shared Task Queue

Name: Host Shared Task Queue
Price: 9 USD
Availability: InStock
Author: Agensi

by Rapa Canola

Async task delegation for AI agents via shared folders—perfect for cross-OS and remote worker coordination.

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Works with the AI tools you already use

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See it in action

You say

Run the video render for project_v1 on the Windows host using the render-queue and tell me when it's finished.

Your agent does

UUID: 8f2b-4e1a... Waiting for result from 'render-queue'... Done! { "status": "success", "output_file": "C:\Renders\project_v1.mp4", "duration_seconds": 145, "artifacts": ["log.txt", "thumb.png"] }

What you get

Trigger Windows-only CLI tools from a Linux-based AI agent environment.Coordinate work across machines behind NAT without exposing public endpoints.Delegate long-running tasks to an external worker to bypass agent timeouts.Implement a robust "human-in-the-loop" approval flow via shared files.

About this skill

What it does

The Shared Task Queue skill provides a robust, low-tech way for an AI agent to delegate tasks to external processes using a shared filesystem directory as the transport. It implements an asynchronous inbox/outbox pattern that works across different machines, operating systems, and user accounts without requiring complex network setup or message brokers.

Why use this skill

Developers often face the "walled garden" problem where an AI agent in a Linux container needs to trigger a process on a Windows host, a legacy machine, or a browser session behind a NAT. This skill bridges that gap. Unlike standard MCP servers or HTTP webhooks, it requires zero infrastructure beyond a shared folder (SMB, NFS, Dropbox, or SyncThing). It handles the messy details of atomic file writes, cross-OS line endings, and polling logic.

Supported tools

Compatible with any watcher process that can read and write JSON files.
Built-in bash scripts for send, wait, receive, and purge operations.
Fallback support for Python 3 when specialized CLI utilities are missing.
Supports shared directories via Samba, SyncThing, NFS, or local mounts.

The Result

The agent submits a structured JSON payload and receives a structured response once the external worker completes the task. All communication is persisted in the filesystem, providing a clear audit log and making failures easy to debug.

How to install

Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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Listed3 months ago

Creator

Rapa Canola

5 skills on Agensi

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