A specialized headless browser server designed for AI agents to navigate the web, interact with elements, and bypass anti-bot detection.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install camofox-browser
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install camofox-browser using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Camofox Browser Automation?
Camofox Browser is a robust, Firefox-based headless automation server specifically engineered for AI agents. It provides a structured API for agents to perform complex web interactions while remaining undetected by sophisticated bot-protection mechanisms. By using this tool within the Openclaw Skills ecosystem, developers can empower their agents with high-fidelity web browsing capabilities, enabling them to navigate, click, type, and extract data just like a human user.
The server is designed with a focus on security and isolation, ensuring that agent activities are compartmentalized via user IDs and session keys. This architecture prevents data leakage between tasks and provides a clean environment for every automation sequence. As a core component for advanced Openclaw Skills, Camofox Browser bridges the gap between static LLM reasoning and dynamic web execution.
Camofox Browser Automation Use Cases
- Automated web research and data scraping for AI agents.
- Bypassing Google CAPTCHAs and sophisticated bot detection during automated tasks.
- Structured web interaction using accessibility trees and stable element references.
- Multi-user session isolation for parallel agent task execution without cookie interference.
- Extracting YouTube transcripts and metadata through specialized backend integrations.
How Camofox Browser Automation Works
- An AI agent initializes a browser tab by sending a POST request to the server, creating an isolated user session.
- The agent navigates to a specific URL or utilizes built-in search macros for platforms like Google, Amazon, or Reddit.
- The server generates a snapshot of the page using an accessibility tree, assigning stable element references like e1 or e2 to interactive components.
- The agent interacts with the page by sending commands such as click, type, or scroll using these stable references.
- The agent repeats the snapshot and interaction cycle to complete complex workflows, with the server managing session timeouts and data persistence.
Camofox Browser Automation Setup
To integrate this capability into your Openclaw Skills workflow, follow these steps:
# Clone the repository and install dependencies
npm install
# Start the automation server locally on port 9377
npm start
# Alternatively, deploy using Docker
docker build -t camofox-browser .
docker run -p 9377:9377 camofox-browser
Camofox Browser Automation Data Schema & Taxonomy
The skill organizes its data and interactions using the following schema:
| Attribute | Type | Description |
|---|---|---|
| userId | String | Isolates cookies and local storage between different agent users. |
| sessionKey | String | Groups tabs by specific conversation or task ID for organizational clarity. |
| tabId | String | A unique identifier for a specific browser tab instance. |
| elementRef | String | Stable identifiers (e.g., e1, e2) representing DOM elements in a snapshot. |
| macro | String | Pre-defined search triggers like @google_search or @wikipedia_search. |
Camofox Browser Automation Advanced Features
- Advanced anti-detection engine powered by Camoufox to bypass modern bot-management solutions.
- Built-in search macros that allow agents to query major platforms without constructing complex URLs.
- Security-hardened architecture that isolates environment variables and child processes to satisfy strict Openclaw Skills scanner rules.
- Integrated YouTube transcript extraction via yt-dlp for media-rich data gathering.
- Prometheus metrics support for real-time monitoring of agent navigation performance and success rates.
SKILL.md
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