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BytesAgain β€Ί Skills β€Ί Gemini Computer Use
πŸ¦€ ClawHub

Gemini Computer Use

by @am-will

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 β†’ function_call β†’ action β†’ function_response), or asks to integrate safety confirmation for risky UI actions.

Versionv1.0.0
Downloads4,270
Installs13
Stars⭐ 5
#legal#gaming#automation#productivity
TERMINAL
clawhub install gemini-computer-use

πŸ“– About This Skill

name: gemini-computer-use description: 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 β†’ function_call β†’ action β†’ function_response), or asks to integrate safety confirmation for risky UI actions.

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

    • πŸ’‘ Examples

    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