learning-spaced-repetition

majiayu000's avatarfrom majiayu000

Design spaced repetition schedules, flashcard systems, and retrieval practice using algorithms like Leitner or SM-2 for long-term retention. Use for memory optimization. Activates on "spaced repetition", "flashcards", "retention", or "Anki-style".

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When & Why to Use This Skill

The Learning Spaced Repetition skill is designed to maximize long-term memory retention by implementing scientifically-proven algorithms like SM-2 and the Leitner system. It automates the creation of flashcard systems and adaptive review schedules, helping users transition information from short-term memory to permanent knowledge through optimized retrieval practice.

Use Cases

  • Language Learning: Automatically generate vocabulary flashcards from word lists and schedule reviews based on difficulty to accelerate fluency.
  • Medical and Professional Exams: Organize vast amounts of factual data, such as anatomy or legal codes, into a structured spaced repetition system for high-stakes certification prep.
  • Academic Study: Convert lecture notes and textbook content into active recall practice sets with predictive retention scheduling.
  • Technical Skill Maintenance: Create review cycles for programming syntax, command-line arguments, or architectural patterns to prevent knowledge decay over time.
namelearning-spaced-repetition
descriptionDesign spaced repetition schedules, flashcard systems, and retrieval practice using algorithms like Leitner or SM-2 for long-term retention. Use for memory optimization. Activates on "spaced repetition", "flashcards", "retention", or "Anki-style".

Learning Spaced Repetition

Optimize long-term retention through scientifically-spaced review schedules.

When to Use: Language learning, medical education, factual knowledge, certification prep

Algorithms: Leitner system, SM-2, adaptive intervals based on performance

CLI: /learning.spaced-repetition --content "vocabulary-list.csv" --algorithm "SM-2"

Output: Flashcard decks, review schedules, retention predictions

Exit Codes: 0=success, 1=invalid algorithm, 2=insufficient content