Adds AnKing-specific study analytics, weakness trackers, a daily exam planner, per-card stats, risk sorting, and browser tools for targeting and unsuspending cards.
Alternatives to π Race Pace π
AnkiWeb addon 850212518
AI answer
Answers are generated by Claude from the catalog data and may contain mistakes.
Similar addons (found without AI)
Adds a panel showing days left until an exam and statistically estimated study time, configured per deck via the settings icon.
Estimates how long today's Anki reviews will take and tracks live progress, pace, and remaining time across all decks based on recent average review times.
Turns Anki reviews into real-time car races against friends, ghosts, or leaderboards, with multiplayer rooms, overlays, group races, stats, and an account requirement.
Turns exam dates into manageable daily new-card targets through multiple independent plans with flexible study days, previews, safe limit restoration, and automatic recalculation, while leaving review scheduling to Anki.
Plans new-card introduction toward a deadline and forecasts card maturation, tracking progress against milestones with consistency metrics and actionable study advice.
Calculates how many cards you must review daily to clear a chosen deck's due backlog, including subdecks, by a target date.
Monitors card activity during Anki reviews to pause or restart a configurable Pomodoro focus timer, show study stats, plan decks around exam dates, and optionally sync sessions.
Turns exam deadlines into adaptive daily card targets per deck, shows on-track progress, builds filtered study decks, and adds optional gamified quests without changing Anki's scheduling.
Automatically adjusts a deck's specific daily new-card limit to meet a chosen deadline, optionally including subdecks and a margin, with previews and automatic recalculations.
Calculates a daily study plan per deck to finish reviews and new cards by a target date, with adjustable intensity, deck selection, simulations, and automatic unique presets.
Predicts remaining study time and finish time using an offline machine-learning model, with customizable progress displays, metrics, CSS styling, and fatigue alerts.