spaced-repetition-teaching

name

spaced-repetition-teaching

description

Adaptive spaced repetition engine using the FSRS-6 algorithm (Free Spaced Repetition Scheduler, Ye et al. 2024). Manages flashcard reviews with scientifically optimal intervals based on memory research. Triggers on: study sessions, flashcard reviews, "what's due today", "review cards", spaced repetition scheduling, and study session management. Developed through the Formation Fellowship technical interview prep program.

Cleanup Review

python scripts/_reset_state.py is an optional cleanup helper for the Spaced Repetition Skill (FSRS-6) skill. It may recursively remove /tmp/_skill_work, /tmp/skill_build. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

Spaced Repetition Skill (FSRS-6)

Adaptive flashcard review system using the FSRS-6 algorithm — the state of the art in spaced repetition scheduling, backed by 130+ years of memory research.

Algorithm: FSRS (Free Spaced Repetition Scheduler) by Ye et al., 2024. Open-source reference: open-spaced-repetition/py-fsrs (MIT).

Origin: Developed and refined through the Formation Fellowship program. The author is not a representative of Formation.

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Card File

Cards live in a user-specified markdown file. If not specified, ask once.

Card Format

Each card is a markdown section (### Title) with metadata:

### Binary Search on Answer Space
- **Priority:** P1
- **Prompt:** "Given items of various sizes and N recipients, find the largest
  portion so everyone gets at least one. Approach?"
- **Answer:** Binary search on the answer space [1, max(items)]. Feasibility
  predicate: sum(item // size for item in items) >= recipients. Return hi.
- **Interrogate:** When would two pointers beat this? What makes the predicate
  monotonic?
- **When to reach for it:** "Maximize/minimize a value subject to a feasibility
  check" — binary search on the answer.
- **FSRS:** d=5.50 s=8.20 reps=3 lapses=0 last=2026-03-11 next=2026-03-19
- **History:** [2026-03-04 G=3(Good), 2026-03-09 G=1(Again), 2026-03-11 G=3(Good)]

FSRS fields:

• d = difficulty [1–10] (lower is easier)
• s = stability in days (≈ days until 90% recall probability)
• reps = total reviews
• lapses = times forgotten (rated Again)
• last / next = last review date / scheduled next review

Rating scale:

• 1 = "Didn't know it" (blanked or completely wrong)
• 2 = "Struggled" (got there but with significant difficulty or errors)
• 3 = "Got it" (recalled correctly with some effort)
• 4 = "Nailed it" (instant, effortless recall)

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Review Methodology

Each review should cycle through multiple modes — not just recall:

1. Recall — Explain the approach without looking (mental rehearsal)
2. Interrogate — Why this approach? Tradeoffs? What changes if requirements change?
3. Rewrite — Code/apply it cold, timed. Notice hesitations.
4. Retain — Revisit 48+ hours later. Can't reproduce cleanly? → Rate Again (1).

❌ Skipping post-recall phases = 80% effort for 50% results.

Priority guide:

• P1: Fundamental, comes up everywhere. Review first.
• P2: Common pattern, transferable. Review second.
• P3: Good to know, niche. Skip if time-capped.

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Scripts

All scripts in scripts/ — pure Python 3.6+, no external dependencies.

Check what's due

python scripts/due_cards.py ~/my-cards.md
python scripts/due_cards.py ~/my-cards.md --all        # include upcoming
python scripts/due_cards.py ~/my-cards.md --date 2026-03-20  # plan ahead

Submit a review

python scripts/review.py ~/my-cards.md "Binary Search" 3
# Ratings: 1="Didn't know it" 2="Struggled" 3="Got it" 4="Nailed it"

Run algorithm self-test

python scripts/fsrs.py

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Handling User Requests

"What's due today?" / "Show my queue"

Run due_cards.py. Present P1 cards prominently.

"I reviewed [card] — rated [X]"

Run review.py. Show updated stability and next interval. If they forgot (Again), normalize it — it's data, not failure.

"Add a new card for [topic]"

Insert a new section in their card file. Do NOT add the FSRS line — it gets created automatically on first review.

Template:

### [Title]
- **Priority:** [P1/P2/P3]
- **Prompt:** "[Question]"
- **Answer:** [Key insight + approach]
- **Interrogate:** [Tradeoffs? What if requirements change?]
- **When to reach for it:** [Pattern/signal that triggers this approach]
- **Added:** [date]
- **History:** []

"How is my retention?" / "Stats"

Parse card file and compute: strong cards (s>30d), struggling cards (lapses>0), 7-day review load forecast.

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Interpreting FSRS Numbers (Advanced)

Most users don't need this — the system handles scheduling automatically. For the curious:

• Stability (s): Days until ~90% recall. s=10 → review in ~10 days.
• Difficulty (d): 1=very easy, 10=very hard. Good cards converge to 3–6.
• After "Didn't know it": Stability drops sharply (e.g., 20d → 3d). Correct behavior.
• After "Nailed it": Stability grows fast. Use sparingly — only for instant recall.
• Key insight: At 90% retention target, interval ≈ stability.

Algorithm Reference

See references/fsrs-algorithm.md for full FSRS math, formulas, and default weights. Algorithm paper: Ye et al., "A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling" (2024).