Skill: ivangdavila/learn (version 1.0.2)

--- name: Learn slug: learn version: 1.0.2 description: Structure and track learning with spaced repetition and active recall across any domain. changelog: Fixed data folder to match slug, removed vague cron reference metadata: {"clawdbot":{"emoji":"🎓","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Data Storage ``` ~/learn/ ├── topics/ # One folder per topic │ └── {topic}/ │ ├── concepts.json # Concepts with SR schedule │ ├── notes.md # Study notes │ └── progress.md # Mastery tracking ├── reviews/ # Due review queue │ └── due.json └── config.json # Preferences ``` Create on first use: `mkdir -p ~/learn/{topics,reviews}` ## Scope This skill: - ✅ Creates learning plans in ~/learn/ - ✅ Tracks concepts with spaced repetition - ✅ Generates quizzes for active recall - ✅ Reminds user when reviews are due (stores schedule in ~/learn/reviews/) - ❌ NEVER accesses external learning platforms without permission - ❌ NEVER stores data outside ~/learn/ ## Quick Reference | Topic | File | |-------|------| | Cognitive principles | `cognition.md` | | Spaced repetition math | `retention.md` | | Verification methods | `verification.md` | ## Core Rules ### 1. Workflow ``` Goal → Plan → Study → Practice → Verify → Review ``` ### 2. Active Recall Only NEVER passive review. Always: - Ask question first, user answers - Then show correct answer - User rates: easy / good / hard / wrong ### 3. Starting a Topic 1. User states what they want to learn 2. Create ~/learn/topics/{topic}/ 3. Break down into concepts 4. Add to spaced repetition queue ### 4. Spaced Repetition In concepts.json: ```json { "concept_name": { "added": "2024-03-15", "interval_days": 1, "next_review": "2024-03-16", "ease_factor": 2.5, "reviews": 0 } } ``` After each review: - Correct → increase interval (×ease_factor) - Incorrect → reset to 1 day ### 5. Verification Before marking "mastered": - Generate 5 questions covering concept - User must answer 4/5 correctly - Track in progress.md (topic folder) ### 6. Configuration In ~/learn/config.json: ```json { "depth": "standard", "learner_type": "practical", "daily_review_limit": 20 } ```

path: cognition.md
content: # Cognition Reference

## Core Principles

### Working Memory
- Capacity: ~4 items at once
- Duration: 15-30 seconds without rehearsal
- **Rule:** Small chunks, never overwhelm

### Active Recall > Passive Review
- Testing yourself beats re-reading by 2-3x
- Retrieval practice strengthens memory
- **Rule:** Always generate answers, don't just recognize

### Spacing Effect
- Distributed practice >> cramming
- Optimal gaps: 1d → 3d → 7d → 21d → 60d
- **Rule:** Use spaced repetition, never mass practice

### Interleaving
- Mix related topics instead of blocking
- Feels harder, works better
- **Rule:** Vary practice, don't drill one thing

### Cognitive Load
- Intrinsic: Material complexity (can't change)
- Extraneous: Bad design (must minimize)
- Germane: Building understanding (maximize)
- **Rule:** Simple presentation, focus on core

## Anti-Patterns

❌ Re-reading highlights
❌ Cramming before deadline
❌ Blocking by topic (all vocab, then all grammar)
❌ Fluency = mastery (easy now ≠ remembered later)
❌ Passive consumption (videos without practice)

## What AI Should Do

- Force retrieval (questions, not explanations)
- Chunk information (max 3-4 concepts before practice)
- Schedule reviews (expanding intervals)
- Vary examples (same concept, different contexts)
- Make connections explicit (link to prior knowledge)


path: retention.md
content: # Retention Reference

## Why We Forget

- **Decay** — Memories weaken without use
- **Interference** — New info overwrites old
- **Retrieval failure** — Info exists, can't access
- **Encoding failure** — Never properly stored

## Forgetting Curve (Ebbinghaus)

- 50% forgotten within 1 hour
- 70% forgotten within 24 hours
- 90% forgotten within 1 week

**Key insight:** Each retrieval flattens the curve.

## Spaced Repetition (SM-2 Algorithm)

Review just before forgetting:
```
1 day → 3 days → 7 days → 14 days → 30 days → 60 days → ...
```

Adjust based on difficulty:
- Rating 5 (perfect): Increase interval × 2.5
- Rating 4 (hesitation): Increase interval × 2
- Rating 3 (difficult): Interval stays similar
- Rating 0-2 (failed): Reset to 1 day

## Active vs Passive

| Passive (Weak) | Active (Strong) |
|----------------|-----------------|
| Re-reading | Self-testing |
| Highlighting | Flashcards |
| Listening again | Teaching others |
| Watching videos | Writing from memory |

## What Works

1. **Spaced retrieval** — Not massed practice
2. **Active recall** — Generate, don't recognize
3. **Interleaving** — Mix topics
4. **Elaboration** — Connect to existing knowledge
5. **Dual coding** — Verbal + visual
6. **Sleep** — Memory consolidates during sleep

## Anti-Patterns

❌ Immediate repetition (feels good, doesn't work)
❌ Passive re-reading
❌ Fixed intervals for everyone
❌ Only reviewing what you know well


path: verification.md
content: # Verification Reference

## Mastery Levels

| Level | What it Tests | Confidence |
|-------|--------------|------------|
| **Recognition** | "Looks familiar" | 🔴 Very low |
| **Recall** | Can state the answer | 🟠 Low |
| **Application** | Solve new problem | 🟡 Medium |
| **Transfer** | Apply to different domain | 🟢 High |
| **Teaching** | Explain so others understand | 🟢🟢 Very high |

## The "Compiler" for Learning

> Can the learner solve a novel problem using the concept, under time pressure, without hints?

This is the minimum bar for "learned."

## Detecting False Confidence

- **Variation testing** — Same concept, different surface features
- **Why probes** — "Walk me through your reasoning"
- **Prediction prompts** — "What happens if...?" before reveal
- **Confidence calibration** — Track predicted vs actual accuracy
- **Edge cases** — Unusual inputs expose shallow understanding

## Understanding vs Memorization

| Memorization | Understanding |
|--------------|---------------|
| Exact phrasing | Paraphrasing correctly |
| Fails on rephrased Q | Handles variations |
| Can't explain "why" | Explains causation |
| No transfer | Applies broadly |

## Verification Methods

1. **Generate, don't select** — Free response > multiple choice
2. **Teach back** — Explain to AI as if teaching
3. **Novel problems** — Not from practice set
4. **Time pressure** — Removes lookup crutch
5. **Spaced verification** — Re-test after days/weeks

## Quiz Protocol

1. Answer without notes
2. Rate confidence (1-5) BEFORE checking
3. Check answer
4. Track calibration over time
5. Focus on: high confidence + wrong (dangerous blind spots)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)