據 AlphaMoat 統計,截至 2026-09,Browser Use Pro 累計下載 2,187 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 4,299 位,在「AI 智慧體」分類(969 個)中排名第 863 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 48 | 1897 | - | - |
| SkillHub ↗ | 148 | 2187 | - | — |
各平台的計數口徑不同,統計時間不一致。
指令碼在本地執行,但逐步推理會將瀏覽上下文傳送給配置的 LLM API。連線真實 Chrome 的 CDP 模式需使用者主動啟動,且可能暴露登入狀態和 Cookie。應優先使用隔離瀏覽器配置,限制允許訪問的域名,避免向提示詞或指令碼放入真實金鑰,並在涉及敏感賬號時關閉視覺截圖;執行前應審查擬執行操作。 需要 Python、browser-use、Playwright 和已配置的模型;不保證站點可訪問或自動化成功。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 2187 | — | - |
| 2026-08 | 1796 | +12.8% | - |
| 2026-07 | 1592 | +19.2% | - |
| 2026-06 | 1336 | +15.1% | - |
| 2026-05 | 1161 | — | - |
| 2026-03 | 622 | — | - |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
--- 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
```bash
test -d ~/browser-use-env && echo "Installed" || echo "Need install"
```
### 2. First-Time Setup (once only)
```bash
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:
```bash
/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:
```bash
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
```python
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
```python
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
```python
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
```python
agent = Agent(
task="Login with x_user and x_pass",
sensitive_data={"x_user": "[email protected]", "x_pass": "S3cret!"},
use_vision=False, # Disable screenshots when handling passwords
llm=llm, browser=browser,
)
```
## Key Parameters
| Parameter | Purpose | Recommended |
|-----------|---------|-------------|
| `use_vision` | AI sees screenshots | True normally, False with passwords |
| `max_steps` | Max actions | 20-30 |
| `max_failures` | Max retries | 3 (default) |
| `flash_mode` | Skip reasoning | True for simple tasks |
| `extend_system_message` | Custom instructions | Add specific guidance |
| `allowed_domains` | Restrict URLs | Use for security |
| `fallback_llm` | Backup LLM | When 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
| LLM | Works | Notes |
|-----|:---:|-------|
| GPT-4o / 4o-mini | ✅ | Best choice, recommended |
| Claude | ✅ | Works well |
| Gemini | ❌ | Structured output incompatible |
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。