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camofox-browser卡莫福克斯浏览器

Agent Skill

camofox-browser 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

4,647

周安装

188

GitHub Stars

公开资料未说明

下载量

1,459
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:camofox-browser(卡莫福克斯浏览器)
来源仓库:https://github.com/sheffk78/camofox-browser
安装命令:
openclaw skills install camofox-browser
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 camofox-browser

简介

无头浏览器服务器使 AI 代理能够创建选项卡、导航、交互以及使用本地或云中的元素引用捕获快照。

SKILL.md

camofox-browser Agent Guide

Headless browser automation server for AI agents. Run locally or deploy to any cloud provider.

Quick Start for Agents

# Install and start
npm install && npm start
# Server runs on http://localhost:9377

Core Workflow

  1. Create a tab → Get tabId
  2. Navigate → Go to URL or use search macro
  3. Get snapshot → Receive page content with element refs (e1, e2, etc.)
  4. Interact → Click/type using refs
  5. Repeat steps 3-4 as needed

API Reference

Create Tab

POST /tabs
{"userId": "agent1", "sessionKey": "task1", "url": "https://example.com"}

Returns: {"tabId": "abc123", "url": "...", "title": "..."}

Navigate

POST /tabs/:tabId/navigate
{"userId": "agent1", "url": "https://google.com"}
# Or use macro:
{"userId": "agent1", "macro": "@google_search", "query": "weather today"}

Get Snapshot

GET /tabs/:tabId/snapshot?userId=agent1

Returns accessibility tree with refs:

[heading] Example Domain
[paragraph] This domain is for use in examples.
[link e1] More information...

Click Element

POST /tabs/:tabId/click
{"userId": "agent1", "ref": "e1"}
# Or CSS selector:
{"userId": "agent1", "selector": "button.submit"}

Type Text

POST /tabs/:tabId/type
{"userId": "agent1", "ref": "e2", "text": "hello world"}
# Add enter: {"userId": "agent1", "ref": "e2", "text": "search query", "pressEnter": true}

Scroll

POST /tabs/:tabId/scroll
{"userId": "agent1", "direction": "down", "amount": 500}

Navigation

POST /tabs/:tabId/back     {"userId": "agent1"}
POST /tabs/:tabId/forward  {"userId": "agent1"}
POST /tabs/:tabId/refresh  {"userId": "agent1"}

Get Links

GET /tabs/:tabId/links?userId=agent1&limit=50

Close Tab

DELETE /tabs/:tabId?userId=agent1

Search Macros

Use these instead of constructing URLs:

MacroSite
@google_searchGoogle
@youtube_searchYouTube
@amazon_searchAmazon
@reddit_searchReddit
@wikipedia_searchWikipedia
@twitter_searchTwitter/X
@yelp_searchYelp
@linkedin_searchLinkedIn

Element Refs

Refs like e1, e2 are stable identifiers for page elements:

  1. Call /snapshot to get current refs
  2. Use ref in /click or /type
  3. Refs reset on navigation - get new snapshot after

Session Management

  • userId isolates cookies/storage between users
  • sessionKey groups tabs by conversation/task (legacy: listItemId also accepted)
  • Sessions timeout after 30 minutes of inactivity
  • Delete all user data: DELETE /sessions/:userId

Running Engines

Camoufox (Default)

npm start
# Or: ./run.sh

Firefox-based with anti-detection. Bypasses Google captcha.

Testing

npm test              # E2E tests
npm run test:live     # Live Google tests
npm run test:debug    # With server output

Docker

docker build -t camofox-browser .
docker run -p 9377:9377 camofox-browser

Key Files

  • server.js - Camoufox engine (routes + browser logic only — NO process.env or child_process)
  • lib/config.js - All process.env reads centralized here
  • lib/youtube.js - YouTube transcript extraction via yt-dlp (child_process isolated here)
  • lib/launcher.js - Subprocess spawning (child_process isolated here)
  • lib/cookies.js - Cookie file I/O
  • lib/metrics.js - Prometheus metrics (lazy-loaded, off by default — set PROMETHEUS_ENABLED=1)
  • lib/request-utils.js - HTTP request classification helpers (actionFromReq, classifyError)
  • lib/snapshot.js - Accessibility tree snapshot
  • lib/macros.js - Search macro URL expansion
  • Dockerfile - Production container

OpenClaw Scanner Isolation (CRITICAL)

OpenClaw's skill-scanner flags plugins that have process.env + network calls (e.g. app.post, fetch, http.request) in the same file, or child_process + network calls in the same file. These patterns suggest potential credential exfiltration.

Rule: No single .js file may contain both halves of a scanner rule pair:

  • process.env lives ONLY in lib/config.js
  • child_process / execFile / spawn live ONLY in lib/youtube.js and lib/launcher.js
  • server.js has the Express routes (app.post, app.get) but ZERO process.env reads and ZERO child_process imports
  • lib/metrics.js has NO process.env and NO HTTP method strings (POST, fetch). Prometheus is lazy-loaded only when PROMETHEUS_ENABLED=1.
  • lib/request-utils.js has HTTP method strings (POST) but NO process.env — safe.
  • When adding new features that need env vars or subprocesses, put that code in a lib/ module and import the result into server.js

Scanner rule details (from src/security/skill-scanner.ts):

  • env-harvesting (CRITICAL): fires when /process\.env/ AND /\bfetch\b|\bpost\b|http\.request/i match the SAME file. Note: the regex is case-insensitive, so string literals like 'POST' and even comments containing process.env will trigger it.
  • dangerous-exec (CRITICAL): child_process import + exec/spawn call in same file
  • potential-exfiltration (WARN): readFile + fetch/post/http.request in same file

This was broken in 1.3.0 (YouTube child_process in server.js), fixed in 1.3.1. Broken again in 1.4.1 (metrics.js had process.env in a comment + 'POST' in actionFromReq), fixed in 1.5.1 by lazy-loading prom-client and splitting actionFromReq into lib/request-utils.js.

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

76.86%
按下载量换算1,121

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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