browser-act/skills

What it solves

BrowserAct provides a browser environment specifically designed for AI agents to perform web automation, data extraction, and account-based workflows. It solves the problem of AI agents being blocked by anti-bot measures, the inability to handle authenticated sessions across parallel tasks, and the inefficiency of processing raw HTML/JSON for LLM reasoning.

How it works

The project provides a set of "Skills" that AI agents can load and execute via shell commands. It operates through three layers of protection to bypass blocks: an environment layer (fingerprint spoofing, TLS rotation), an execution layer (CAPTCHA solving), and a human layer (remote handoff for manual intervention).

It offers three browser modes:

• Chrome: Reuses local login states.
• Stealth Privacy: Fresh fingerprints and proxies for batch scraping.
• Stealth Fixed Identity: Stable fingerprints and IPs for maintaining account identities.

To optimize for LLMs, it uses a compact indexed text format for page state rather than raw DOM, allowing agents to interact with elements by index (e.g., click 3).

Who it’s for

Developers building AI agents (using tools like Claude Code, Cursor, or Gemini CLI) who need their agents to interact with the real web, manage multiple accounts, or scrape protected content without being detected as bots.

Highlights

• Agent-Centric Design: Uses indexed interaction and compact text output to reduce token usage and simplify reasoning.
• Anti-Blocking Suite: Combines stealth fingerprints, TLS rotation, residential proxies, and automated CAPTCHA solving.
• Human-in-the-Loop: Includes a remote-assist feature that allows a human to take over a browser session via a live URL when an agent gets stuck.
• Zero-Interference Concurrency: Supports independent cookies and fingerprints for parallel tasks to prevent cross-contamination.
• Skill Forge: A tool that automatically discovers website APIs and data patterns to generate reusable scraping skills without manual coding.