memory-retrieval-learning▌
lyndonkl/claude · updated Apr 8, 2026
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Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.
Memory, Retrieval & Learning
Table of Contents
Purpose
Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.
When to Use
Use memory-retrieval-learning when you need to:
Exam & Certification Prep:
- Study for professional certifications (AWS, CPA, PMP, bar exam, medical boards)
- Prepare for academic exams (SAT, GRE, finals)
- Master substantial material over weeks/months
- Retain knowledge for high-stakes tests
Professional Learning:
- Learn new technology stack or programming language
- Master company product knowledge
- Study industry regulations and compliance
- Transition to new career field
- Learn software tools and methodologies
Language Learning:
- Master vocabulary and grammar rules
- Learn verb conjugations and sentence patterns
- Study pronunciation and idioms
- Build conversational fluency
Skill Mastery:
- Learn complex procedures (medical, technical, safety)
- Master formulas, equations, or algorithms
- Memorize taxonomies or classification systems
- Study historical facts, dates, or sequences
What Is It
Memory-retrieval-learning applies cognitive science research on how humans learn durably:
Key Principles:
- Spaced Repetition: Review material at increasing intervals (1 day, 3 days, 7 days, 14 days, 30 days)
- Retrieval Practice: Test yourself actively rather than passively re-reading
- Interleaving: Mix different topics/types rather than blocking by type
- Elaboration: Connect new knowledge to existing understanding
Quick Example:
Learning Spanish verb conjugations:
Week 1: Learn 20 new verbs → Test yourself same day
Week 1: Review those 20 verbs after 1 day → Test
Week 1: Review after 3 days → Test
Week 2: Review after 7 days → Test + Add 20 new verbs
Week 3: Review old verbs after 14 days → Test + Continue new verbs
Week 5: Review after 30 days → Test
This combats the forgetting curve by reviewing just before you'd forget.
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
Create active recall mechanisms: flashcards, practice problems, mock tests, self-quizzing. Avoid passive techniques (highlighting, re-reading). See Common Patterns for domain-specific approaches.
Step 4: Execute daily learning sessions
Follow the schedule: new material in morning (peak alertness), reviews in afternoon/evening. Use retrieval practice consistently. Log what's difficult for extra review. For advanced techniques like interleaving or desirable difficulties, see resources/methodology.md.
Step 5: Track progress and adjust
Measure retention with self-tests. Adjust review frequency based on performance (struggle more = review sooner). Update schedule as needed. Validate using resources/evaluators/rubric_memory_retrieval_learning.json.
Common Patterns
Exam Preparation (3-6 months):
- Phase 1 (60% time): Initial learning + comprehension
- Phase 2 (30% time): Spaced review + retrieval practice
- Phase 3 (10% time): Mock exams + weak area focus
- Use: Professional certifications, academic finals, bar exam
Language Learning (ongoing):
- Daily: 10 new vocabulary words + review old words due today
- Weekly: Grammar lesson + interleaved practice with prior lessons
- Monthly: Conversation practice integrating all learned material
- Use: Spanish, Mandarin, French, any language mastery
Technology/Job Skill (3-12 weeks):
- Week 1-2: Fundamentals + hands-on practice
- Week 3-6: Advanced concepts + spaced review of fundamentals
- Week 7+: Real projects + systematic review of challenging concepts
- Use: Learning Python, React, AWS, data analysis
Medical/Technical Procedures:
- Day 1: Learn procedure steps + immediate practice
- Day 2: Retrieval practice without notes
- Day 4: Practice + add edge cases
- Day 8: Full simulation
- Day 15, 30: Refresh to maintain
- Use: Clinical skills, safety protocols, lab techniques
Bulk Memorization (facts, dates, lists):
- Create spaced repetition flashcard deck
- Review cards daily (Anki algorithm or similar)
- Retire cards after 5+ successful recalls
- Add mnemonic devices for difficult items
- Use: Anatomy, geography, historical dates, pharmacology
Guardrails
Avoid Common Mistakes:
- ❌ Passive re-reading or highlighting → Use active retrieval instead
- ❌ Cramming (massed practice) → Use spaced repetition
- ❌ Blocking by topic (all topic A, then all topic B) → Use interleaving
- ❌ Over-confidence after initial learning → Test yourself repeatedly
- ❌ No tracking → Measure retention to adjust schedule
Realistic Expectations:
- Forgetting is normal and necessary for strong memory consolidation
- Initial struggles with retrieval are productive ("desirable difficulties")
- Expect 20-40% forgetting between reviews (that's the sweet spot)
- Spaced repetition feels less productive than massing, but works better
- Plan for 2-3x more time than you think you need
Time Management:
- Daily consistency > marathon sessions
- Minimum 15-20 min/day more effective than 2 hours weekly
- Peak retention: 25 min study → 5 min break → repeat
- Review sessions should be shorter than initial learning sessions
- Build buffer for life interruptions (illness, travel, deadlines)
When to Seek Help:
- Material isn't making sense after 3+ attempts → Get instructor/expert help
- Retention remains below 60% after 3 review cycles → Reassess study method
- Burnout or motivation collapse → Reduce daily load, add intrinsic rewards
- Test anxiety interfering → Address anxiety separately from memory techniques
Quick Reference
Resources:
resources/template.md- Learning plan template with schedulingresources/methodology.md- Advanced techniques for complex learning goalsresources/evaluators/rubric_memory_retrieval_learning.json- Quality criteria
Output:
- File:
memory-retrieval-learning.mdin current directory - Contains: Learning goals, material breakdown, review schedule, retrieval methods, tracking system
Success Criteria:
- Spaced repetition schedule covers entire timeline
- Retrieval practice methods defined for all material types
- Daily time commitment is realistic and sustainable
- Tracking mechanism in place to measure retention
- Schedule includes buffer for setbacks
- Validated against quality rubric (score ≥ 3.5)
Evidence-Based Techniques:
- Spacing Effect: Reviews at 1, 3, 7, 14, 30 days
- Testing Effect: Self-test > re-study for long-term retention
- Interleaving: ABCABC > AAABBBCCC for transfer and discrimination
- Elaboration: Connect to prior knowledge, explain to others
- Dual Coding: Combine verbal + visual representations
How to use memory-retrieval-learning on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add memory-retrieval-learning
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches memory-retrieval-learning from GitHub repository lyndonkl/claude and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate memory-retrieval-learning. Access the skill through slash commands (e.g., /memory-retrieval-learning) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★46 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
Registry listing for memory-retrieval-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aanya Abebe· Dec 28, 2024
Keeps context tight: memory-retrieval-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Diego Flores· Dec 24, 2024
Registry listing for memory-retrieval-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ren Mehta· Dec 8, 2024
We added memory-retrieval-learning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Neel Huang· Nov 27, 2024
memory-retrieval-learning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Jin Shah· Nov 23, 2024
memory-retrieval-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Nov 19, 2024
Keeps context tight: memory-retrieval-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amelia Patel· Nov 19, 2024
Registry listing for memory-retrieval-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aditi Diallo· Nov 15, 2024
Keeps context tight: memory-retrieval-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ren Huang· Oct 18, 2024
memory-retrieval-learning has been reliable in day-to-day use. Documentation quality is above average for community skills.
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