A portable, zero-dependency skill for cross-platform desktop automation and computer-use tasks across macOS, Windows, and Linux.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cross-platform-computer-use-skill
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 cross-platform-computer-use-skill 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 Cross-Platform Computer-Use Skill?
This skill provides a top-level, portable computer-use implementation designed to work natively across different operating systems. Unlike other solutions, it functions without relying on local Claude installations or private native modules, making it a highly flexible addition to the Openclaw Skills ecosystem.
By bundling standalone runtimes for macOS, Windows, and Linux, it ensures that your AI agents can interact with the desktop environment regardless of the host OS. It maintains a clean installation by selecting the correct platform payload at runtime and preserving a standalone bootstrap model to manage dependencies locally.
Cross-Platform Computer-Use Skill Use Cases
- Automating GUI interactions on diverse operating systems without local dependencies.
- Building platform-agnostic automation scripts for Openclaw Skills users.
- Executing desktop-level tasks like typing and clicking across macOS, Windows, and Linux.
- Developing portable agents that require direct operating system interaction without manual environment configuration.
How Cross-Platform Computer-Use Skill Works
- The skill bundles specific runtimes for macOS, Windows, and Linux into a single package located in the skills directory.
- Upon execution, a helper script (Bash or PowerShell) detects the current host platform to resolve the active project path.
- The system initializes a standalone bootstrap model on the first run, creating a dedicated virtualenv.
- Public Python dependencies are installed automatically within the isolated environment to ensure zero external conflicts.
- The skill interacts with the OS via platform-specific APIs, such as X11 for Linux or native GUI tools for macOS and Windows.
- MCP layer integration handles action gating while the runtime reports capabilities like screenshot filtering.
Cross-Platform Computer-Use Skill Setup
To install and configure this skill within the Openclaw Skills directory structure, follow these steps:
# Identify current project path
PLATFORM_PATH=$(bash ~/.codex/skills/cross-platform-computer-use-skill/scripts/current-project.sh)
cd "$PLATFORM_PATH"
# Install dependencies and build the platform-specific project
npm install
npm run build
To run the CLI on the resolved platform:
node dist/cli.js
Cross-Platform Computer-Use Skill Data Schema & Taxonomy
The skill uses a structured layout within the .codex directory to manage platform-specific assets and scripts.
| Directory/File | Description |
|---|---|
/project/platforms/macos |
macOS specific runtime, scripts, and compiled assets. |
/project/platforms/windows |
Windows specific runtime and PowerShell integration logic. |
/project/platforms/linux |
Linux runtime targeting X11 environments for desktop interaction. |
/scripts/current-project.sh |
Bash helper for automatic platform detection on Unix-like systems. |
/scripts/current-project.ps1 |
PowerShell helper for platform detection on Windows hosts. |
Cross-Platform Computer-Use Skill Advanced Features
- Zero local Claude dependency for maximum portability and ease of deployment.
- Standalone bootstrap model that isolates Python environments per platform to prevent dependency hell.
- Dynamic platform resolution using dedicated helper scripts for both Bash and PowerShell environments.
- Support for Openclaw Skills MCP layer action gating and advanced screenshot filtering protocols.
- Explicit platform-specific limitation reporting to ensure the AI agent understands host constraints (e.g., Wayland vs X11).
SKILL.md
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