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.
- License 许可
- MIT
- 最近更新
- 2026-04-11
- Stars
- ★ 3
- Forks
- 0
01 前言 Front Matter
- 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 → function_call → action → function_response), or asks to integrate safety confirmation for risky UI actions.
02 资源文件 3
- 其他env.example409 B
- 参考文档references/google-computer-use.md636 B
- 脚本scripts/computer_use_agent.py11.3 KB
03 SKILL.md 正文
Gemini Computer Use
Quick start
-
Source the env file and set your API key:
cp env.example env.sh $EDITOR env.sh source env.sh -
Create a virtual environment and install dependencies:
python -m venv .venv source .venv/bin/activate pip install google-genai playwright playwright install chromium -
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)
- Capture a screenshot and send the user goal + screenshot to the model.
- Parse
function_callactions in the response. - Execute each action in Playwright.
- If a
safety_decisionisrequire_confirmation, prompt the user before executing. - Send
function_responseobjects containing the latest URL + screenshot. - 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
--excludeto 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
04 同仓库更多 Skills
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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
swarm-planner
[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.
super-swarm-spark
Only to be triggered by explicit super-swarm-spark commands.
role-creator
Create and install Codex custom agent roles in ~/.codex/config.toml, generate role config files, enforce supported keys, and guide users through required role inputs (model, reasoning effort, developer_instructions).
read-github
Read and search GitHub repository documentation via gitmcp.io MCP service. **WHEN TO USE:** - User provides a GitHub URL - User mentions a specific repo in owner/repo format - User asks "what does this repo do?", "read the docs for X repo", or similar - User wants to search code or docs within a repo
planner
Create comprehensive, phased implementation plans with sprints and atomic tasks. Use when user says: "make a plan", "create a plan", "plan this out", "plan the implementation", "help me plan", "design a plan", "draft a plan", "write a plan", "outline the steps", "break this down into tasks", "what's the plan for", or any similar planning request. Also triggers on explicit "/planner" or "/plan" commands.