What problem does it solve?
This Skill eliminates the inefficiency of token-heavy, brittle browser automation for AI agents, which traditionally relies on parsing full raw HTML and fragile CSS selectors that break with minor page layout changes. It reduces the context required for web interactions from thousands of tokens to just 200-400 per step, making automated browser tasks faster, more reliable, and far less costly to run.
Core Features & Use Cases
- Ref-driven snapshot workflow: Uses compact accessibility tree snapshots with stable @eN element references that auto-update after page changes, eliminating selector breakage.
- Full browser task coverage: Supports all common web automation tasks including navigation, element interaction (click, fill, type, select), data extraction, screenshots, tab management, authentication flows, parallel sessions, and network mocking.
- Real-world use case: Automate end-to-end workflows like logging into a SaaS platform, filling out multi-step forms, and extracting structured dashboard data without manual selector adjustments or context bloat.
Quick Start
Use the core agent-browser skill to open the target website, capture an interactive snapshot of the page, interact with elements using their assigned references, and extract the required data from the updated page.
