Search

PythonReactSEOPDF
OpenClaw

gemini-computer-use

Build and run Gemini 2.5 Computer Use browser-control agents with Playwright. Use when a user wants to automate web browser tasks via the Gemini Computer Use model, needs an agent loop (screenshot → function_call → action → function_response), or asks to integrate safety confirmation for risky UI actions.

am-will0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install am-will/gemini-computer-use

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

Gemini Computer Use

Quick start

  1. Source the env file and set your API key:

    cp env.example env.sh
    $EDITOR env.sh
    source env.sh
    
  2. Create a virtual environment and install dependencies:

    python -m venv .venv
    source .venv/bin/activate
    pip install google-genai playwright
    playwright install chromium
    
  3. Run the agent script with a prompt:

    python scripts/computer_use_agent.py \
      --prompt "Find the latest blog post title on example.com" \
      --start-url "https://example.com" \
      --turn-limit 6
    

Browser selection

  • Default: Playwright's bundled Chromium (no env vars required).
  • Choose a channel (Chrome/Edge) with COMPUTER_USE_BROWSER_CHANNEL.
  • Use a custom Chromium-based executable (e.g., Brave) with COMPUTER_USE_BROWSER_EXECUTABLE.

If both are set, COMPUTER_USE_BROWSER_EXECUTABLE takes precedence.

Core workflow (agent loop)

  1. Capture a screenshot and send the user goal + screenshot to the model.
  2. Parse function_call actions in the response.
  3. Execute each action in Playwright.
  4. If a safety_decision is require_confirmation, prompt the user before executing.
  5. Send function_response objects containing the latest URL + screenshot.
  6. Repeat until the model returns only text (no actions) or you hit the turn limit.

Operational guidance

  • Run in a sandboxed browser profile or container.
  • Use --exclude to block risky actions you do not want the model to take.
  • Keep the viewport at 1440x900 unless you have a reason to change it.

Resources

  • Script: scripts/computer_use_agent.py
  • Reference notes: references/google-computer-use.md
  • Env template: env.example

Source information

Source
github
Repository
openclaw/skills
Path
skills/am-will/gemini-computer-use
Version
—
Commit
0b71494672d1670f439f834868755bcc5da6421a
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# Gemini Computer Use

## Quick start

1. Source the env file and set your API key:

   ```bash
   cp env.example env.sh
   $EDITOR env.sh
   source env.sh
   ```

2. Create a virtual environment and install dependencies:

   ```bash
   python -m venv .venv
   source .venv/bin/activate
   pip install google-genai playwright
   playwright install chromium
   ```

3. Run the agent script with a prompt:

   ```bash
   python scripts/computer_use_agent.py \
     --prompt "Find the latest blog post title on example.com" \
     --start-url "https://example.com" \
     --turn-limit 6
   ```

## Browser selection

- Default: Playwright's bundled Chromium (no env vars required).
- Choose a channel (Chrome/Edge) with `COMPUTER_USE_BROWSER_CHANNEL`.
- Use a custom Chromium-based executable (e.g., Brave) with `COMPUTER_USE_BROWSER_EXECUTABLE`.

If both are set, `COMPUTER_USE_BROWSER_EXECUTABLE` takes precedence.

## Core workflow (agent loop)

1. Capture a screenshot and send the user goal + screenshot to the model.
2. Parse `function_call` actions in the response.
3. Execute each action in Playwright.
4. If a `safety_decision` is `require_confirmation`, prompt the user before executing.
5. Send `function_response` objects containing the latest URL + screenshot.
6. Repeat until the model returns only text (no actions) or you hit the turn limit.

## Operational guidance

- Run in a sandboxed browser profile or container.
- Use `--exclude` to block risky actions you do not want the model to take.
- Keep the viewport at 1440x900 unless you have a reason to change it.

## Resources

- Script: `scripts/computer_use_agent.py`
- Reference notes: `references/google-computer-use.md`
- Env template: `env.example`

Related skills

OpenClaw

openbotx

openbotx

An open-source platform for orchestrating AI agents — secure, simple, and built for everyone. Multi-agent, real-time task board, web control panel, skills system, browser automation, multi-provider, scheduler,...

OpenClaw

zsxq-digest

sealiu1997

OpenClaw skill for 知识星球 / ZSXQ digests with token-first auth and browser recovery.

OpenClaw

browser-use-skill

abczsl520

🌐 OpenClaw skill for Browser-Use — AI-powered browser automation for complex multi-step workflows (login, form filling, scraping, posting)