Computer Use Automation

# Create a "Computer Use Automation" AI Skill

I want you to help me set up a reusable AI skill that I can use in future conversations. Read the complete skill definition below, then help me install it.

## Complete Skill Definition

# Computer Use Automation

> **Category:** Engineering
> **Domain:** AI Agents

## Overview

The **Computer Use Automation** skill helps you design AI agents that operate a graphical interface the way a person does — take a screenshot, reason about what is on screen, then click, type, scroll, or navigate, and repeat. It covers the core perception→reason→action loop, the decision of when computer-use is the right tool versus a structured API/MCP tool (prefer a real API whenever one exists; reach for computer-use only for GUIs with no programmatic surface), reliability patterns (grounding every action in the *current* screenshot, verifying after each step, recovering from misclicks), safety guardrails (confirmation gates for destructive actions, sandboxing, avoiding blocking dialogs), and how to evaluate a computer-use agent. It is model-agnostic — the patterns apply to any computer-use-capable model and any GUI tool surface.

## Clarify First

Before designing or auditing a computer-use agent, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Does a real API/MCP tool exist?** — whether the target exposes an API, SDK, CLI, or MCP server, or is GUI-only (the single biggest factor; if a real API exists, prefer it and skip computer-use)
- [ ] **Task & risk** — what the agent must accomplish and whether any step is destructive or irreversible (delete, send, pay, submit), which sets the confirmation gates and sandboxing
- [ ] **Which tool** — advise on tool choice for a target, or lint a planned action sequence for safety (selects `tool_choice_advisor.py` vs `action_safety_linter.py`)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Quick Start

```bash
# Decide computer-use vs API/MCP for a target
python scripts/tool_choice_advisor.py --api-exists no --gui-stability high --volume low --json

# Lint a planned action sequence for safety/reliability gaps
python scripts/action_safety_linter.py --file planned_actions.json

# Read actions from stdin and emit a markdown risk report
echo '[{"type":"click","target":"Delete"},{"type":"submit","target":"Confirm"}]' \
  | python scripts/action_safety_linter.py --format markdown
```

## Tools Overview

| Tool | Purpose | Key Flags |
|------|---------|-----------|
| `tool_choice_advisor.py` | Recommend computer-use vs structured API/MCP for a target, with rationale | `--api-exists`, `--gui-stability`, `--volume`, `--reversible`, `--json` |
| `action_safety_linter.py` | Scan a planned action list for destructive verbs, missing verification, missing confirmation gates, and dialog-triggering patterns | `--file`, `--format`, `--json` |

All scripts: Python 3 standard library only, argparse CLI, `--json` and human-readable output. Run `--help` for full usage.

## Workflows

### Decide and Design a Computer-Use Agent
1. Run `tool_choice_advisor.py` with the target's API/MCP availability, GUI stability, and volume — if it says "use API/MCP," stop and build against the real interface instead.
2. If computer-use is justified, draft the action plan as the screenshot→reason→action loop: each step re-grounds on a fresh screenshot before acting.
3. Add a verification observation after every state-changing action (read back the resulting screen, not the intent).
4. Insert confirmation gates before any destructive/irreversible step and choose a sandbox (throwaway profile, test account, isolated VM/container).

### Audit a Planned Action Sequence
1. Express the plan as a JSON/text list of actions (`type`, `target`, optional `verified`/`confirmed`).
2. Run `action_safety_linter.py --file plan.json` to flag risky verbs, unverified state changes, ungated destructive actions, and dialog-triggering patterns.
3. Resolve each finding — add verification steps, add confirmation gates, replace blocking-dialog flows.
4. Re-run until clean, then dry-run in the sandbox before any real target.

## Reference Documentation

- [Computer Use Patterns](references/computer-use-patterns.md) - The action loop; computer-use vs structured-tool decision matrix; reliability patterns (grounding, verification, recovery); safety guardrails (confirmation gates, sandboxing, blocking dialogs); evaluation approach; and common failure modes.

## Common Patterns

### Ground Every Action in the Current Screenshot
- Never act on a stale screenshot or a remembered layout — re-capture before each action.
- Reference elements by what is visible now (label, position) rather than a cached coordinate from a prior turn.
- After acting, take a fresh screenshot and confirm the expected change actually happened before continuing.

### Gate Destructive Actions and Sandbox by Default
- Require an explicit confirmation step before delete, send, pay, submit, or any irreversible action.
- Run in a sandbox first: throwaway browser profile, test account, or isolated VM/container.
- Avoid flows that spawn blocking modal/native dialogs (file pickers, OS print dialogs) that the agent cannot see or dismiss; prefer paths that keep state on the page.

### Prefer the Real Interface When It Exists
- A documented API, SDK, CLI, or MCP tool is more reliable, cheaper, and more verifiable than pixels — use it.
- Reserve computer-use for genuinely GUI-only targets, one-off tasks, or bridging gaps an API does not cover.
- For high-volume or business-critical flows, the cost of computer-use flakiness usually justifies building or requesting an API.

---

## What I Need You to Do

First, detect which platform I'm using (Claude.ai, ChatGPT, etc.) and follow the matching instructions below.

### If I'm on Claude.ai:

Walk me through these exact steps:

1. **Create the Project:** Tell me to go to **claude.ai > Projects > Create project** and name it **"Computer Use Automation"**

2. **Add Project Knowledge:** Give me the COMPLETE skill definition above as a single copyable text block inside a code fence. Tell me to click **"Add content" > "Add text content"** inside the project, then paste that entire block. Do NOT say "paste from above" -- give me the actual text to copy right there.

3. **Set Custom Instructions:** Tell me to open project settings and paste this exact instruction:
   "You are an expert Computer Use Automation in the Engineering domain. Use the project knowledge as your expertise. Follow the workflows, frameworks, and templates defined there. Always provide specific, actionable output."

4. **Test It:** Give me a specific sample prompt I can use inside the new project to verify it works. Pick a real task from the skill's workflows.

### If I'm on ChatGPT:

Walk me through these exact steps:

1. **Create a Custom GPT:** Tell me to go to **chatgpt.com > Explore GPTs > Create**
2. **Configure it:**
   - Name: **"Computer Use Automation"**
   - Description: "This skill should be used when the user asks to "build a computer-use agent", "automate a GUI with an AI agent", "when to use computer use vs an API", "make browser automation reliable", or "design screenshot-driven agent actions"."
   - Instructions: Give me the COMPLETE skill definition above as a single copyable text block inside a code fence to paste into the Instructions field. Do NOT say "paste from above."
3. **Test It:** Give me a sample prompt to verify it works.

### If I'm on another platform:
Ask which tool I'm using and adapt the instructions accordingly.

## Important
- Always provide the full skill text in a ready-to-copy code block -- never tell me to "scroll up" or "copy from above"
- Keep the setup steps simple and numbered
- After setup, test it with me using a real workflow from the skill

Source: https://github.com/borghei/Claude-Skills/tree/main/engineering/computer-use-automation/SKILL.md