Adds dozens of extra customizable statistical graphs to Anki's stats screen, including retention, load, lapse, repetition, time, interval, FSRS, and time-machine charts.
Alternatives to 📶Accumulated Retention Graph (FSRS ETK)
AnkiWeb addon 1971437351
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Similar addons (found without AI)
Displays an interactive heatmap calendar and stats on Anki's main screen, tracking reviews, retention, speed, focus, workload forecasts, plus themes and background photos.
Generates an HTML dashboard summarizing daily, weekly, or monthly study sessions with retention, FSRS metrics, SVG growth graphs, narrative insights, and optional LLM-generated reflections from local collection data.
Adds a draggable review-screen widget showing each card's memory health as one of seven color statuses plus configurable stats and designs, compatible with FSRS and SM-2.
Reschedules Anki cards using FSRS review histories, postpones or advances selected cards, balances daily workload, schedules breaks, disperses sibling cards, flattens backlogs, and reports memory and learning-step statistics.
Simplifies Anki's FSRS simulator by letting users simulate a deck, its subdecks, or a preset, inspect included cards and counts, adjust settings, and compare runs on one graph.
Splits review sessions into configurable chunks and displays two linked progress bars with color-coded intervals, timers, session tracking, and optional FSRS-based retention coloring.
Displays deck-browser graphs of answer speed versus retention, a learning forecast, and new-card counts with time-remaining estimates.
Displays recent Anki review activity as a cached, customizable bar graph directly on the Decks screen, with configurable date ranges, optional daily goal line, and hover details.
Transfers scheduling data, complete review history, and FSRS memory state from one card to another via browser right-click options or keyboard shortcuts.
Breaks large daily review workloads into smaller blocks with timed healthy breaks, FSRS-based session recommendations, a movable progress bar, auto-hide/sync, notifications, webhooks, and customizable per-deck settings.
Predicts remaining study time and finish time using an offline machine-learning model, with customizable progress displays, metrics, CSS styling, and fatigue alerts.