Anki add-on: Anki Leech Manager - Saga

An improved leech manager and finder to quickly discover which cards are taking the most of your review time for a more effective, more accurate, and faster suspension workflow. The main columns I'd expect users to make use of are LifetimeIFR and RecentIFR, neither of which Anki currently exposes well in it's current leech management system. Custom Columns - Summarized Logic 1. IFR (Initial Fail Rate%) Purpose : Measures how often a card is failed on the first review of each day Calculation : For each day with reviews, checks if the chronologically first review was failed by the user Interpretation : High IFR: Likely leech card requiring attention Low IFR: Well-learned card N/A: No review history available 2. Age Purpose : Shows days since first review Usage : Identify how long you've been working with a card, used to help let newer cards mature before filtering or analyzing for leech status 3. DailyTimeSpent(avg) Purpose : Shows average daily time investment per card, over it's lifetime Display : Time in seconds (e.g., "8.7") Calculation : Total review time divided by card age (days since it's first review) Interpretation : High values indicate time-intensive cards that may need modification or deletion, unless recent improvements. Leech Manager Menu Functions Export Leech Info Exports all leech data to a pipe-delimited text file for statistical analysis Filter by Age or Number of Unique Days Reviewed Filter browser view to cards with age >= specified days Default: 7 days (configurable via dialog) Automatically excludes unreviewed cards Preserves current deck and search context Example: If viewing "deck:Japanese", filter applies only to Japanese deck cards Used to speed up filtering and leech suspension workflow Contributing Contributions are welcome! I'm not around on GitHub much, so ping me on Discord.

GitHub repository: sagaskoyere/anki-leech-manager
Repository description: A tool to help identify "leeches" in Anki, an SRS program for efficient suspension workflows.
Languages: Python