Memory Retrieval Learning

Builds evidence-based learning plans using spaced repetition, retrieval practice, and interleaving to maximize long-term retention. Walks you through defining goals, breaking down material, scheduling reviews at intervals like 1, 3, 7, 14, 30 days, and designing active recall methods instead of passive re-reading. The workflow is thorough with templates and rubrics included, covering everything from three-month exam prep to ongoing language learning. The guardrails are honest about what actually works, like how forgetting 20-40% between reviews is the sweet spot and daily consistency beats marathon sessions. It's basically a structured way to avoid cramming and build knowledge that sticks.

skills.sh installs: 492
GitHub stars: 193
last updated: Aug 21, 2026

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Install

$ npx -y skills add lyndonkl/claude --skill memory-retrieval-learning --agent claude-code

Installs into .claude/skills of the current project.

Files

Workflow

Copy this checklist and track your progress:

Learning Plan Progress:
- [ ] Step 1: Define learning goals and timeline
- [ ] Step 2: Break down material and create schedule
- [ ] Step 3: Design retrieval practice methods
- [ ] Step 4: Execute daily learning sessions
- [ ] Step 5: Track progress and adjust

Step 1: Define learning goals and timeline

Clarify what needs to be learned, by when, and how much time is available daily. Identify success criteria (pass exam, demonstrate skill, etc). Use resources/template.md to structure your plan.

Step 2: Break down material and create schedule

Chunk material into learnable units. Calculate spaced repetition schedule based on timeline. Plan initial learning + review cycles. For complex schedules or long timelines (6+ months), see resources/methodology.md for advanced scheduling techniques.

Step 3: Design retrieval practice methods

SKILL.md on GitHub