Master 1 fundamental loop: Perception-Reasoning-Action.
Build AI agents that interact with computers like humans. Start building today!
Vision-Language Models
Desktop Control
Agentic Loops
Screen Perception
Action Execution
About This Skill
Master the core architecture of computer use agents, focusing on the 4-stage Perception-Reasoning-Action loop and the critical feedback mechanism for robust desktop automation.
Quick Start
1Install Anthropic and PyAutoGUI
2Initialize the ComputerUseAgent class
3Define your first perception-reasoning-action loop
Example Command
python agent_run.py --model claude-sonnet-4
Core Capabilities
Perception-Reasoning-Action Loop
The fundamental architecture of computer use agents: observe screen, reason about next action, execute action, repeat.
Usage Examples
Before
Manual clicking and searching through tabs.
After
Autonomous vision-based navigation and data extraction.
Input
Fill out a complex Excel form.
Output
Agent identifies input fields via vision and types text via keyboard emulation.
Input
Automate a legacy desktop application.
Output
Agent uses pixel-based recognition to interact with non-standard UI elements.
SKILL.md
---
name: computer-use-agents
description: 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-source alternatives.
risk: unknown
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---
# Computer Use Agents
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-source alternatives. Critical focus on
sandboxing, security, and handling the unique challenges of vision-based control.
## 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
Frequently Asked Questions
FAQ
What tool compatibility is required?
The skill works best with Anthropic's Claude models and Python libraries like PyAutoGUI and PIL.
Who is the target audience?
AI developers, automation engineers, and researchers working on agentic workflows.
How does this differ from standard RPA?
Unlike traditional RPA which uses selectors, this uses vision-based reasoning to interact with screens like a human.
Is there language support for the agent?
The agent's reasoning is driven by Vision-Language Models (VLMs), supporting most major human languages.
What are the expected results in terms of latency?
Expect a 1-5 second 'thinking' pause during the reasoning phase as the model analyzes the screenshot.
Discussion
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