# Individual Spacing Algorithm Explainer

Explain and configure individual spacing algorithms using student performance data and...

- Repository: https://github.com/GarethManning/education-agent-skills (939 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add GarethManning/education-agent-skills --skill individual-spacing-algorithm-explainer -g -a claude-code -y`
Codex: `npx skills add GarethManning/education-agent-skills --skill individual-spacing-algorithm-explainer -g -a codex -y`
Cursor: `npx skills add GarethManning/education-agent-skills --skill individual-spacing-algorithm-explainer -g -a cursor -y`
Gemini CLI: `npx skills add GarethManning/education-agent-skills --skill individual-spacing-algorithm-explainer -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add GarethManning/education-agent-skills`

## Description

Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Individual Spacing Algorithm Explainer

## What This Skill Does

Generates a personalised spaced repetition schedule for a specific set of knowledge items based on student performance data, and explains the algorithm logic in teacher-friendly terms. This skill does NOT execute the spacing algorithm, execution requires a system like Anki, Quizlet, SuperMemo, or Kaku's own retention engine. What this skill does is DESIGN and EXPLAIN: given what students know and what they're forgetting, it produces a concrete review schedule with clear rationale for why each interval was chosen. The core insight from spacing research is that the optimal review interval depends on the desired retention period and the item's current memory strength. Ebbinghaus (1885) established that forgetting follows a predictable curve, steep initially, then gradually flattening. Cepeda et al. (2006) demonstrated in a comprehensive meta-analysis that spacing review across time produces substantially better long-term retention than massing the same amount of practice into a single session. The practical challenge is that the optimal interval is different for every student and every item, which is where personalisation becomes essential. AI is specifically valuable here because calculating optimal intervals across dozens of items for individual students is computationally trivial for a machine but practically impossible for a teacher doing it manually.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-learning-science/individual-spacing-algorithm-explainer/SKILL.md