EffortLess Layered Menu Visibility is a WordPress plugin designed to give site administrators precise control over which navigation menu items appear to visitors. It addresses the need for dynamic menu customization by allowing different menu items to be shown or hidden based on specific criteria such as login status, user role, device type, page type, or archive context.
The plugin integrates directly with the WordPress navigation menu editor, enabling users to configure visibility rules without requiring custom code. These rules are evaluated on every page load, ensuring that menu items adapt in real time to the visitor's context. This approach allows for tailored navigation experiences, such as displaying certain links only to logged-in users, showing different options to users with specific roles, or adjusting menus based on whether the visitor is on a mobile device or viewing a particular type of page or archive.
EffortLess Layered Menu Visibility is suitable for WordPress site owners and administrators who want more granular control over their site's navigation structure. Its straightforward interface is designed to be accessible directly from within the familiar WordPress menu editor, making it a practical solution for those who wish to adjust menu visibility without adding complexity or relying on custom development.
As a plugin available through the WordPress.org Plugin Directory, it is delivered for use on WordPress sites.
EffortLess Layered Menu Visibility is an UI/UX & prototyping project. It focuses on allowing WordPress site owners to control which navigation menu items are visible to different users and contexts without coding. It is built as a consumer product for wordPress site owners. It is available for free. EffortLess Layered Menu Visibility is available on the web.
domclic builds and maintains EffortLess Layered Menu Visibility, and it first shipped in 2026. Among its 5 catalogued features are menu visibility rules, user role targeting, and device detection. The interface is available in English and Chinese.
Summary written by a language model from the project’s public pages.
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