Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmemory-retrieval-learningExecute the skills CLI command in your project's root directory to begin installation:
Fetches memory-retrieval-learning from lyndonkl/claude and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate memory-retrieval-learning. Access via /memory-retrieval-learning in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.
Use memory-retrieval-learning when you need to:
Exam & Certification Prep:
Professional Learning:
Language Learning:
Skill Mastery:
Memory-retrieval-learning applies cognitive science research on how humans learn durably:
Key Principles:
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.
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.
Exam Preparation (3-6 months):
Language Learning (ongoing):
Technology/Job Skill (3-12 weeks):
Medical/Technical Procedures:
Bulk Memorization (facts, dates, lists):
Avoid Common Mistakes:
Realistic Expectations:
Time Management:
When to Seek Help:
Resources:
resources/template.md - Learning plan template with schedulingresources/methodology.md - Advanced techniques for complex learning goalsresources/evaluators/rubric_memory_retrieval_learning.json - Quality criteriaOutput:
memory-retrieval-learning.md in current directorySuccess Criteria:
Evidence-Based Techniques:
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Registry listing for memory-retrieval-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: memory-retrieval-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for memory-retrieval-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
We added memory-retrieval-learning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
memory-retrieval-learning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
memory-retrieval-learning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: memory-retrieval-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for memory-retrieval-learning matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: memory-retrieval-learning is the kind of skill you can hand to a new teammate without a long onboarding doc.
memory-retrieval-learning has been reliable in day-to-day use. Documentation quality is above average for community skills.
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