skills/memory-retrieval-learning/SKILL.md
Creates evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, interleaving, and elaboration. Guides through goal definition, material breakdown, review scheduling, and progress tracking. Use when long-term knowledge retention is needed, studying for exams or certifications, learning new job skills or technology, mastering substantial material, combating forgetting, or when user mentions studying, memorizing, learning plans, spaced repetition, flashcards, active recall, or durable learning.
npx skillsauth add lyndonkl/claude memory-retrieval-learningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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:
testing
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.