computer-use-agents

Build sandboxed AI agents for desktop and browser tasks via visual perception.

Updated Jul 18, 2026
One-click install
npx skills add https://github.com/arthrod/conejo-skills --skill computer-use-agents-arthrod
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: computer-use-agents
Source: https://github.com/arthrod/conejo-skills/tree/main/skills/computer-use-agents
Command: npx skills add https://github.com/arthrod/conejo-skills --skill computer-use-agents-arthrod

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anthropic, pillow, pyautogui, playwright, requests.

What problem does it solve?

This skill addresses the complexity of building AI agents capable of interacting with desktop environments, providing a structured approach to vision-based control while enforcing critical security and sandboxing protocols.

Core Features & Use Cases

  • Perception-Reasoning-Action Loop: Implements a robust pipeline for screen observation, decision-making, and mouse/keyboard execution.
  • Sandboxed Execution: Provides Docker-based isolation patterns to minimize the blast radius of autonomous agents.
  • Multi-Modal Control: Supports both pixel-based vision control (Anthropic Computer Use) and structured browser automation (Playwright) for optimal task performance.
  • Use Case: Deploy an agent to perform complex multi-step web research or desktop application workflows that require visual verification, while ensuring the agent remains isolated from host system credentials and files.

Quick Start

Use the computer-use-agents skill to initialize a sandboxed environment and execute a task to search for weather information in a browser.

Frequently Asked Questions about computer-use-agents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build autonomous AI agents for desktop automation using vision models?▼

You can build autonomous AI agents for desktop automation by implementing a perception-reasoning-action loop that uses vision models for screen observation and PyAutoGUI for mouse and keyboard execution.

What is the best way to sandbox AI agents performing browser automation?▼

The best way to sandbox AI agents performing browser automation is using Docker-based isolation patterns to limit resources and isolate the agent from host system credentials and files.

Does Playwright work with vision-based computer use agents?▼

Yes, Playwright works with vision-based computer use agents by providing structured browser automation alongside pixel-based vision control, enabling optimal performance for complex web data extraction.

Can I use Anthropic computer use for multi-step web research tasks?▼

Yes, you can use Anthropic computer use for multi-step web research tasks by deploying agents that perform complex workflows requiring visual verification within a security-hardened container configuration.

Why do I need Docker to run computer use agents?▼

You need Docker to run computer use agents to enforce strict sandboxing, minimize the blast radius of autonomous operations, and provide security-hardened isolation from your host system.

What are the limitations of pixel-based vision control in automated agents?▼

Pixel-based vision control in automated agents requires strict sandboxing and resource limiting to ensure safe operation, and may need structured browser automation as a complementary approach for optimal task performance.