mok-it/feladatsor-frontend

AI Summary: Implement the creation and modification of alerts for exercises using the provided `ExerciseAlertInput` type. This involves creating GraphQL mutations (`insertExerciseAlert`, `updateExerciseAlert`) and corresponding React components to handle user interaction within the existing frontend application. The alert viewing functionality is already in place.

AI Summary: Integrate a Google Form link for bug reporting into the existing React frontend application. The link should be prominently displayed but not intrusive. Consider also adding links to the frontend and backend issue trackers to reduce duplicate reports.

AI Summary: Implement a UI feature to allow users to export all backend data as an Excel file. This involves using the existing `exportExcel` GraphQL mutation which returns a download URL. Consider adding a UI element to display and manage a list of previous exports, although this requires backend integration and is out of scope for this task.

AI Summary: Modify the existing edit profile modal in a React application to increase its size on desktop screens. The current modal is too small and needs resizing for better usability on larger displays. This likely involves adjusting CSS styles within the React component responsible for rendering the modal.

AI Summary: Relocate the popup snackbar in a React application from its current overlapping position to the bottom center of the screen and add a close button to it. This involves modifying the snackbar's styling and adding a close button component.

AI Summary: Implement a feature to display the total number of search results when users search for exercises within the MÖK feladatsor-frontend application. This involves modifying the existing search functionality to count and display the number of matching exercises.

AI Summary: Implement a skeleton loader on the Tags page of the feladatsor-frontend application to provide visual feedback to the user while the tags are loading. This involves creating a loading indicator component and integrating it into the Tags page's rendering logic, likely using a conditional rendering approach based on the loading state of the tag data.