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spaced-repetition

Use when scheduling and executing review sessions at scientifically-calibrated expanding intervals for long-term retention.

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Quellinformationen

Repository
yugash007/edu-agent-skills
Letzte Quellaktivität
18. Mai 2026 um 16:48
Erkannte Sprache von SKILL.md
Englisch
Sterne
7
Forks
2

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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
spaced-repetition
description
Use when scheduling and executing review sessions at scientifically-calibrated expanding intervals for long-term retention.
version
1.1.0
authors
["edu-agent-skills contributors"]
tags
["productivity","spaced-repetition","retention","scheduling"]
status
stable
# Purpose Schedule review of previously-learned concepts at expanding intervals to exploit the spacing effect. Manages due dates, adjusts intervals based on recall quality, and surfaces overdue items before they decay. # Activation - Review session is due based on schedule. Learner initiates review. `flashcards` deck has due items. Sustained learning track (3+ sessions) needing interval maintenance. - **Skip if**: one-shot session with no continuity. Concept not yet learned. Learner declines scheduling. - **Routing**: overdue items take priority at session start. Coordinate with `flashcards` for card-level scheduling. Feed interval data to `learning-memory`. # Inputs - Flashcard/item schedule with due dates, learner's current session, items from `learning-memory`. # Interval Algorithm (Simplified SM2) Each item has an **interval** (days) and **ease factor** (EF, 1.3–2.5). Initial: 1 day → 3 days → then formula. | Score | Label | Interval Rule | EF Change | |---|---|---|---| | 0 | Failed | Reset to 1 day | EF -= 0.2 (min 1.3) | | 1 | Hard | Stay at current | EF -= 0.1 | | 2 | Good | Interval × EF | No change | | 3 | Easy | Interval × EF × 1.3 | EF += 0.1 (max 2.5) | Mastery threshold: EF > 2.4, interval > 60 days, 5 consecutive successes → archive. # Workflow 1. **Detect Due Items** — Check `next_review ≤ today`. Sort by overdue duration (most overdue first). Report count. 2. **Scope Session** — ≤10 due: review all. >10: prioritize by overdue + weak-area overlap, cap at 15. Report deferrals. 3. **Execute Review** — Present front, wait for learner response, reveal back. Self-score: Failed/Hard/Good/Easy. Compute new interval immediately. Never reveal answer before attempt. 4. **Handle Failures** — Score 0: reset to 1 day, re-test at end of current session. Failed 3 sessions in a row: flag for `misconception-detector`. 5. **Update Schedule** — Output updated schedule. Show items due in next 7 days. Warn about upcoming review spikes. 6. **Onboard New Items** — Fresh concept → add at interval=1 day. Confirm concept is understood first (not still unclear). # Rules - DO: never reveal the answer before learner attempts. - DO: coach learners on what "Good" vs "Easy" means for self-scoring calibration. - DO: stagger new item additions to prevent review spikes. - DO: archive mastered items (meet threshold) — don't review indefinitely. - DON'T: review items that aren't due — respect the schedule. - DON'T: let sessions exceed 15 items — defer the rest. - DON'T: add items to schedule before they're genuinely understood. - DON'T: frame review sessions as tests — it's memory maintenance. # Output Session start: due count + overdue count + estimated time. Per-item: topic, question, learner response, answer, score, next review date. Session close: score distribution (easy/good/hard/failed), failed items list, next 7-day schedule. Format naturally. # Checklist - [ ] Overdue items prioritized first. - [ ] Session capped at 15 items. - [ ] Answer not revealed before learner attempt. - [ ] Failed items re-tested within same session.
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