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    host-shared-task-queue

    by Rapa Canola

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

    Updated May 2026
    Security scanned
    One-time purchase

    $9

    One-time purchase

    ⚡ Also available via Agensi MCP — your AI agent can load this skill on demand via MCP. Learn more →

    Included in download

    • Trigger Windows-only CLI tools from a Linux-based AI agent environment.
    • Coordinate work across machines behind NAT without exposing public endpoints.
    • terminal automation included
    • Includes example output and usage patterns
    • Instant install

    See it in action

    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"]
}

    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.

    📖 Learn more: Best DevOps & Deployment Skills for Claude Code →

    Use Cases

    • 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.

    Reviews

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    Only users who have downloaded or purchased this skill can leave a review.

    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell

    Allowed Hosts

    0x67108864.github.io

    File Scopes

    host-shared-task-queue/**

    Tags

    devops
    automation
    windows-integration
    task-queue
    bash

    Creator

    Rapa Canola

    Frequently Asked Questions

    Learn More About AI Agent Skills

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