spaced-repetition-teaching

Schedule flashcard reviews using the FSRS-6 algorithm with Markdown files.

1|Updated Mar 13, 2026

Search Tags:#learning #memory #spaced repetition #flashcards #study #fsrs

Ready to run

npx skills add https://github.com/tylerbittner/agent-skills --skill spaced-repetition-teaching

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: spaced-repetition-teaching
Source: https://github.com/tylerbittner/agent-skills/tree/main/spaced-repetition-teaching
Command: npx skills add https://github.com/tylerbittner/agent-skills --skill spaced-repetition-teaching

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you learn and retain information more effectively by using a scientifically optimized spaced repetition system, ensuring you review material at the most opportune moments to maximize long-term memory.

Core Features & Use Cases

Adaptive Scheduling: Leverages the FSRS-6 algorithm to calculate optimal review intervals based on your recall performance.
Structured Card Format: Organizes learning material into prompts, answers, and review strategies within Markdown files.
Review Modes: Encourages multi-modal review beyond simple recall, including interrogation and application.
Use Case: A student preparing for exams can input their notes as flashcards and rely on the Skill to schedule reviews, ensuring they focus on difficult concepts and retain information long-term.

Quick Start

Use the spaced-repetition-teaching skill to show me cards that are due today from my file '~/my-cards.md'.

Frequently Asked Questions about spaced-repetition-teaching

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I schedule flashcard reviews using the FSRS algorithm?▼

The FSRS-6 algorithm calculates optimal spaced repetition intervals by analyzing your recall performance on flashcards. It schedules reviews at the most opportune moments to maximize long-term memory retention without manual interval tracking.

How do I add new flashcards to my spaced repetition study schedule?▼

You add new flashcards by creating Markdown files with structured metadata for prompts, answers, and review strategies. Python scripts then process these files to integrate the new cards into your active FSRS review schedule.

Does FSRS spaced repetition support different review modes beyond simple recall?▼

Yes, the system supports multi-modal review modes including interrogation and application. You can structure Markdown flashcards with specific review strategies that go beyond simple recall to test deeper understanding and application of the material.

What's the best way to analyze memory retention for a spaced repetition flashcard deck?▼

You analyze memory retention by running the Skill's Python scripts, which process FSRS parameters and review history stored in Markdown files. This generates retention analysis outputs that help you identify difficult concepts and optimize your study schedule.

Can I use Markdown files to organize and store spaced repetition flashcard data?▼

Yes, Markdown files are the primary format for storing flashcard data. The Skill uses specific metadata fields within Markdown files to organize prompts, answers, and FSRS parameters, enabling Python scripts to manage card reviews and retention analysis.