agent-browser

Automating browser interactions for AI agents in dynamic SPAs requires deterministic element targeting via refs and accessible-tree snapshots to ensure repeatable actions. This Skill provides JSON-based interactive snapshots to guide AI decision-making in complex workflows.

Ref-based element selection for headless browser automation targets elements using stable reference names instead of fragile CSS selectors. This mechanism ensures repeatable targeting in multi-step workflows where DOM structures shift dynamically.

Taking an interactive JSON snapshot of a headless browser session captures the accessible tree and element refs for AI-driven decision making. The snapshot identifies key ref names for actions like clicking and filling form fields.

Isolating browser sessions and managing multiple tabs for automated testing is supported through comprehensive browser controls. The Skill provides session isolation, multi-tab and multi-frame support, and state persistence capabilities.

Headless browser automation with network control and cookies supports multi-step workflows by allowing comprehensive browser controls. You can manage cookies, network traffic, and local storage while maintaining verifiable state across complex interactions.

Using accessible-tree snapshots for AI browser automation may face limitations when handling elements not exposed in the accessibility tree. Complex SPAs with shadow DOM or canvas elements might require alternative targeting approaches if refs are unavailable.