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browser-use-pro浏览器使用专业版

Agent Skill

browser-use-pro 用于处理浏览器自动化、网页检查和页面信息提取,适合在 OpenClaw 中需要让 Agent 打开页面、读取网页或验证前端流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

21,403

周安装

910

GitHub Stars

公开资料未说明

下载量

7,498
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:browser-use-pro(浏览器使用专业版)
来源仓库:https://github.com/abczsl520/browser-use-pro
安装命令:
openclaw skills install browser-use-pro
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install browser-use-pro

简介

browser-use-pro 增强版浏览器自动化框架支持复杂流程。

  • 当内置工具无法完成登录等操作时启用。
  • 提供高级状态管理与错误重试机制。browser-use-pro 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 可能增加 token 消耗与执行时间开销。
  • 建议先试用免费方案再决定是否升级付费版。

SKILL.md

name
browser-use
description
AI-powered browser automation for complex multi-step web workflows. Uses Browser-Use framework when OpenClaw's built-in browser tool can't handle login flows, anti-bot sites, or 5+ step sequences.

Browser-Use — AI Browser Automation

Security & Privacy

  • No credential logging: Passwords are handled via Browser-Use's sensitive_data parameter — the LLM never sees real credentials, only placeholder tokens.
  • User-initiated Chrome connection: CDP mode (connecting to real Chrome) is opt-in and requires the user to manually launch Chrome with debug flag. The skill never silently connects to running browsers.
  • All packages are open-source: Dependencies are browser-use (38k+ ⭐ on GitHub), playwright (by Microsoft), and langchain-openai — all widely audited open-source tools.
  • Local execution only: Scripts run locally on the user's machine. No data is sent to any server except the configured LLM API for step-by-step reasoning.
  • Domain restriction available: Use allowed_domains parameter to restrict which websites the agent can visit.
  • No telemetry: This skill does not collect, store, or transmit any usage data.

When to Use Browser-Use vs Built-in Tool

| Scenario | Built-in tool | Browser-Use | |----------|:-:|:-:| | Screenshot / click one button | ✅ Free & fast | ❌ Overkill | | 5+ step workflow (login→navigate→fill→submit) | ❌ Breaks easily | ✅ | | Anti-bot sites (real Chrome needed) | ❌ | ✅ | | Batch repetitive operations | ❌ | ✅ |

Cost: Browser-Use calls an external LLM per step (costs money + slower). Use built-in tool for simple actions.

Execution Flow

1. Check Environment

test -d ~/browser-use-env && echo "Installed" || echo "Need install"

2. First-Time Setup (once only)

python3 -m venv ~/browser-use-env
source ~/browser-use-env/bin/activate
pip install browser-use playwright langchain-openai
playwright install chromium

3. Choose Mode

  • Mode A — Built-in Chromium: For simple automation or when detection doesn't matter. Runs immediately.
  • Mode B — Real Chrome CDP: For anti-bot sites or when user's login session is needed. Requires user action.

Mode B setup — prompt user:

Please quit Chrome completely (Mac: Cmd+Q), then tell me "done"

After user confirms:

/Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome --remote-debugging-port=9222 &

Verify: curl -s http://127.0.0.1:9222/json/version

4. Write Script and Run

Write script to user's workspace, then:

source ~/browser-use-env/bin/activate
python3 script_path.py

5. Report Results

Return results to user. On failure, follow the troubleshooting tree below.

Script Template

import asyncio
from browser_use import Agent, ChatOpenAI, Browser

async def main():
    # LLM — any OpenAI-compatible API
    llm = ChatOpenAI(
        model="gpt-4o-mini",
        api_key="<YOUR_API_KEY>",  # From env var or user config
        base_url="https://api.openai.com/v1",
    )

    # Mode A: Built-in Chromium
    browser = Browser(headless=False, user_data_dir="~/.browser-use/task-profile")
    # Mode B: Real Chrome (user must launch with --remote-debugging-port=9222)
    # browser = Browser(cdp_url="http://127.0.0.1:9222")

    agent = Agent(
        task="Detailed step-by-step task description (see guide below)",
        llm=llm, browser=browser,
        use_vision=True, max_steps=25,
    )
    result = await agent.run()
    print(result)

asyncio.run(main())

Task Writing Guide

✅ Good: Specific steps

task = """
1. Open https://www.reddit.com/login
2. Enter username: x_user
3. Enter password: x_pass
4. Click login button
5. If CAPTCHA appears, wait 30s for user to complete
6. Navigate to https://www.reddit.com/r/xxx/submit
7. Enter title: xxx
8. Enter body: xxx
9. Click submit
"""

❌ Bad: Vague

task = "Post something on Reddit"

Tips

  • Keyboard fallback: Add "If button can't be clicked, use Tab+Enter"
  • Error recovery: Add "If page fails to load, refresh and retry"
  • Sensitive data: Use placeholders + sensitive_data parameter

Credential Security

agent = Agent(
    task="Login with x_user and x_pass",
    sensitive_data={"x_user": "real@email.com", "x_pass": "S3cret!"},
    use_vision=False,  # Disable screenshots when handling passwords
    llm=llm, browser=browser,
)

Key Parameters

ParameterPurposeRecommended
use_visionAI sees screenshotsTrue normally, False with passwords
max_stepsMax actions20-30
max_failuresMax retries3 (default)
flash_modeSkip reasoningTrue for simple tasks
extend_system_messageCustom instructionsAdd specific guidance
allowed_domainsRestrict URLsUse for security
fallback_llmBackup LLMWhen primary is unstable

Troubleshooting

Detected as automation?
  └→ Switch to Mode B (real Chrome)

CAPTCHA / human verification?
  └→ Prompt user to complete manually, add wait time in task

LLM timeout?
  └→ Set fallback_llm or use faster model

Action succeeded but no effect (e.g. post not published)?
  └→ 1. Check if platform anti-spam blocked it (common with new accounts)
     2. Add explicit confirmation steps to task

Website UI changed, can't find elements?
  └→ Browser-Use auto-adapts, but add fallback paths in task

LLM Compatibility

LLMWorksNotes
GPT-4o / 4o-miniBest choice, recommended
ClaudeWorks well
GeminiStructured output incompatible

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

75.51%
按下载量换算5,662

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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