agent-browser-automation

What This Skill Does

The agent-browser-automation skill provides a high-performance, native Rust-based headless browser automation tool specifically optimized for AI agents. By utilizing the Chrome DevTools Protocol (CDP) directly, it bypasses the overhead of heavy runtimes like Node.js or Playwright. This tool is designed to allow AI models to perceive and interact with the web as a human would, utilizing accessibility-first snapshots that map UI elements to unique @refs identifiers, making it exceptionally reliable for agentic workflows.

Installation

To integrate this skill into your OpenClaw environment, use the command: clawhub install openclaw/skills/skills/adisinghstudent/agent-browser-automation. Depending on your environment, you may need to ensure your system has appropriate Chrome/Chromium binaries. Run agent-browser install upon the initial setup to ensure the correct browser engine is downloaded for your specific OS. For Linux users, use the --with-deps flag to pull necessary system dependencies.

Use Cases

• Automated Web Research: Navigate to specific URLs, extract text content, and summarize page data for research reports.
• Form Submission: Reliably interact with complex web forms by identifying input fields via accessibility snapshots and filling them programmatically.
• Visual Verification: Take high-fidelity screenshots of rendered pages to verify CSS layouts or confirm the state of a web application after performing actions.
• Dynamic Scraping: Interact with modern SPAs (Single Page Applications) that require JS execution, which static scrapers often fail to handle.

Example Prompts

1. "Open https://www.google.com, search for 'latest AI trends', and extract the titles of the first three results."
2. "Go to the contact page on example.com, fill out the email field @e5 with '[email protected]', and click the submit button @e8."
3. "Navigate to the OpenClaw documentation site, take a full-page screenshot, and save it as 'docs_preview.png'."

Tips & Limitations

• Efficiency: Always prioritize the snapshot command before performing actions. Using @refs is significantly more stable than selectors because they are tied to the accessibility tree.
• Environment: Because this tool uses a native binary, it is extremely resource-efficient compared to browser automation tools running inside a virtual machine or heavy runtime container.
• Limitations: While powerful, this tool is intended for headless execution. It is not designed for traditional manual browsing; it is optimized specifically for programmatic consumption by AI agents. Ensure your agent is configured to handle the JSON or plain text output from the CLI to maintain seamless control flow.