MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

Computer Use Agents

Computer Use Agents is an data AI skill with a core value of Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. It helps developers solve real-world problems in the data domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-so...

Last verified on: 2026-07-07

Quick Facts

Category data
Works With Claude, ChatGPT, Cursor
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-07
Risk Level Low
mkdir -p ./skills/computer-use-agents && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/computer-use-agents/SKILL.md -o ./skills/computer-use-agents/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Computer Use Agents


Patterns


Perception-Reasoning-Action Loop


The fundamental architecture of computer use agents: observe screen,

reason about next action, execute action, repeat. This loop integrates

vision models with action execution through an iterative pipeline.


Key components:

1. PERCEPTION: Screenshot captures current screen state

2. REASONING: Vision-language model analyzes and plans

3. ACTION: Execute mouse/keyboard operations

4. FEEDBACK: Observe result, continue or correct


Critical insight: Vision agents are completely still during "thinking"

phase (1-5 seconds), creating a detectable pause pattern.



**When to use**: ['Building any computer use agent from scratch', 'Integrating vision models with desktop control', 'Understanding agent behavior patterns']


python
from anthropic import Anthropic
from PIL import Image
import base64
import pyautogui
import time

class ComputerUseAgent:
    """
    Perception-Reasoning-Action loop implementation.
    Based on Anthropic Computer Use patterns.
    """

    def __init__(self, client: Anthropic, model: str = "claude-sonnet-4-20250514"):
        self.client = client
        self.model = model
        self.max_steps = 50  # Prevent runaway loops
        self.action_delay = 0.5  # Seconds between actions

    def capture_screenshot(self) -> str:
        """Capture screen and return base64 encoded image."""
        screenshot = pyautogui.screenshot()
        # Resize for token efficiency (1280x800 is good balance)
        screenshot = screenshot.resize((1280, 800), Image.LANCZOS)

        import io
        buffer = io.BytesIO()
        screenshot.save(buffer, format="PNG")
        return base64.b64encode(buffer.getvalue()).decode()

    def execute_action(self, action: dict) -> dict:
        """Execute mouse/keyboard action on the computer."""
        action_type = action.get("type")

        if action_type == "click":
            x, y = action["x"], action["y"]
            button = action.get("button", "left")
            pyautogui.click(x, y, button=button)
            return {"success": True, "action": f"clicked at ({x}, {y})"}

        elif action_type == "type":
            text = action["text"]
            pyautogui.typewrite(text, interval=0.02)
            return {"success": True, "action": f"typed {len(text)} chars"}

        elif action_type == "key":
            key = action["key"]
            pyautogui.press(key)
            return {"success": True, "action": f"pressed {key}"}

        elif action_type == "scroll":
            direction = action.get("direction", "down")
            amount = action.get("amount", 3)
            scroll = -amount if direction == "down" else amount
            pyautogui.scroll(scroll)
            return {"success": True, "action": f"scrolled {dir

Sandboxed Environment Pattern


Computer use agents MUST run in isolated, sandboxed environments.

Never give agents direct access to your main system - the security

risks are too high. Use Docker containers with virtual desktops.


Key isolation requirements:

1. NETWORK: Restrict to necessary endpoints only

2. FILESYSTEM: Read-only or scoped to temp directories

3. CREDENTIALS: No access to host credentials

4. SYSCALLS: Filter dangerous system calls

5. RESOURCES: Limit CPU, memory, time


The goal is "blast radius minimization" - if the agent goes wrong,

damage is contained to the sandbox.



**When to use**: ['Deploying any computer use agent', 'Testing agent behavior safely', 'Running untrusted automation tasks']


python
# Dockerfile for sandboxed computer use environment
# Based on Anthropic's reference implementation pattern

FROM ubuntu:22.04

# Install desktop environment
RUN apt-get update && apt-get install -y \
    xvfb \
    x11vnc \
    fluxbox \
    xterm \
    firefox \
    python3 \
    python3-pip \
    supervisor

# Security: Create non-root user
RUN useradd -m -s /bin/bash agent && \
    mkdir -p /home/agent/.vnc

# Install Python dependencies
COPY requi

🎯 Best For

  • UI designers
  • Product designers
  • Claude users
  • ChatGPT users
  • Cursor users

💡 Use Cases

  • Generating component mockups
  • Creating design system tokens
  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or ChatGPT and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Computer Use Agents to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does this work with Figma?

Some design skills integrate with Figma plugins. Check the Works With section for supported tools.

How do I install Computer Use Agents?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/computer-use-agents/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Skipping usability testing

AI-generated designs should be validated with real users before development.

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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