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...
Quick Facts
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']
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 {dirSandboxed 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']
# 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
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
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
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
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.