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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 来源安装数☆ 0 仓库 Stars

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install am-will/gemini-computer-use

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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

来源信息

Source
github
Repository
openclaw/skills
Path
skills/am-will/gemini-computer-use
Version
—
Commit
0b71494672d1670f439f834868755bcc5da6421a
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

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`

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