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computer-use-agents skill

/skills/computer-use-agents

This skill helps you build AI agents that control computers and GUI elements securely and effectively across vision-based tasks.

npx playbooks add skill omer-metin/skills-for-antigravity --skill computer-use-agents

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
1.4 KB
---
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. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when "computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation, Claude computer, OpenAI Operator, browser agent, visual agent, RPA with AI, " mentioned. 
---

# Computer Use Agents

## Identity



## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Overview

This skill helps you design and build AI agents that control desktop applications and browsers by viewing screens, moving cursors, clicking, and typing—emulating human interactions. It covers proprietary approaches like Anthropic's Computer Use and OpenAI's Operator, plus open-source alternatives, with a critical focus on sandboxing, security, and vision-based control challenges. The goal is practical, secure, and auditable agents for GUI automation and RPA-style tasks.

How this skill works

The skill describes patterns for creating agents that use pixel-level vision, OCR, and DOM-aware inputs to perceive a screen, then map perceptions to low-level actions (mouse, keyboard, window management). It emphasizes a reference-driven workflow: follow established creation patterns, use the sharp-edge diagnostics to anticipate failures, and validate inputs against strict validation rules before execution. It also prescribes sandboxing, permission models, and telemetry to contain risks and provide observability.

When to use it

  • Automating multi-step desktop workflows that require visual reasoning across apps
  • Building browser agents that must interact with rendered content where DOM access is limited
  • Implementing RPA tasks that need resilient visual fallbacks and error recovery
  • Prototyping agents based on Claude Computer Use, OpenAI Operator, or open-source visual agent frameworks
  • Handling tasks where keystroke/click auditing and strong sandboxing are required

Best practices

  • Design with least privilege: run agents in isolated sandboxes and grant only necessary UI access
  • Prioritize deterministic inputs: prefer structured APIs/DOM when available, fall back to vision+OCR only when needed
  • Implement multi-step validation: check goals against validation rules before performing destructive actions
  • Build robust failure modes: detect UI drift, use retries with recovery steps, and surface human-in-the-loop prompts
  • Log all actions, inputs, and rationale for auditability and postmortem analysis

Example use cases

  • Filling forms across legacy desktop apps where no API exists using vision-guided clicks and typed inputs
  • Automating cross-tab browser flows when extensions or DOM hooks are unreliable
  • Creating a secure operator that executes high-sensitivity tasks within a confined VM with strict telemetry
  • Testing visual regressions by having an agent interact with UI elements and report differences
  • Onboarding tasks: guiding users through setup steps by controlling a browser and documenting each action

FAQ

How do I minimize security risks when giving an agent control of my desktop?

Use strong sandboxing, run agents in isolated VMs or containers, limit file and network access, require explicit consent for sensitive operations, and keep comprehensive action logs for auditing.

When should I use vision-based controls vs. API/DOM automation?

Prefer API/DOM automation for reliability and security; use vision-based controls only when APIs are unavailable or the UI is dynamic and requires visual reasoning, and design fallbacks and validations accordingly.