Anki add-on: AnkiCram

Anki add-on for cramming your decks Features • Usage • Compatibility • GitHub • Website 🎯 Overview Anki Cram is an Anki add-on that lets you loop and rebuild decks infinitely without ruining your long-term spaced repetition data. Perfect for: 📚 Last-minute exam preparation 🔄 Intensive review sessions 🎯 Focused studying on specific decks 📊 Tracking your cramming progress Demo Video ✨ Features 🚀 Core Features Infinite Loop Mode : Failed cards instantly return to the queue for immediate retry Smart Deck Selection : Search and select from all your decks with an intuitive interface Tag Filtering : Focus on specific cards by filtering with tags Session Statistics : Real-time tracking of: ⏱️ Session duration ✅ Cards reviewed 🧠 Retention rate 🔄 Failed cards count 🎮 Usage Starting a Cram Session Launch AnkiCram Click Tools → AnkiCram in the menu bar Select Your Deck Browse or search for the deck you want to cram Use the search bar for quick filtering Selected deck will be highlighted in purple Configure Options ✅ Infinite Loop : Enable to retry failed cards immediately 💾 Keep deck after session : Preserve the cram deck when done 🏷️ Tag Filter : Enter tags to focus on specific cards (comma-separated) Start Cramming Click "Start Cram Session" A filtered deck named AnkiCram - [Your Deck] will be created Begin reviewing cards as normal During Your Session Corner Widget : A floating widget appears in the bottom-left showing "AnkiCram" Click it to view session statistics Access rebuild and stop options Session Stats : Monitor your progress in real-time Time elapsed Cards reviewed Retention percentage Re-loop count Rebuild Deck : Allows you to do the Deck Again Ending Your Session Click the corner widget Click "End Session" 🔧 Compatibility ✅ Anki 2.1.50+ ✅ Qt6 (Anki 2.1.55+) ✅ Windows, macOS, Linux ✅ All deck types 📄 License This project is licensed under the MIT License.

GitHub repository: pmvrsi/ankicram
Repository description: An Anki add-on that lets you loop and rebuild decks infinitely without ruining your long-term spaced repetition data.
Languages: Python