Skill: ivangdavila/exam (version 1.0.0)

--- name: Exam description: Generate practice tests, flashcards, study schedules, and timed simulations from any study material. --- ## What This Skill Does Complete exam preparation from your content: - **Practice tests** — Multiple choice, short answer, essay questions - **Flashcards** — Key concepts for spaced repetition, Anki-exportable - **Simulations** — Timed mock exams matching real format - **Gap analysis** — Identify weak areas, prioritize weak topics - **Study schedules** — Realistic calendars based on exam date and availability - **Summaries** — Condense chapters to 1-2 pages of exam-relevant content - **Concept maps** — Visualize how topics connect - **Quick review sheets** — Last-minute "cheat sheets" for 30-min pre-exam review Works for: university exams, certifications (AWS, PMP, etc.), standardized tests, professional licensing. --- ## Quick Reference | Task | Load | |------|------| | Question generation patterns | `questions.md` | | Flashcard formats and strategies | `flashcards.md` | | Timed simulation setup | `simulations.md` | | Performance tracking | `tracking.md` | --- ## Core Workflow ### 1. Provide Source Material User shares: notes, textbook chapters, slides, documentation, past exams. ### 2. Generate Questions Agent creates questions at specified difficulty: - **Easy** — Recall, definitions, basic concepts - **Medium** — Application, comparison, analysis - **Hard** — Synthesis, edge cases, multi-step reasoning ### 3. Practice & Track User answers, agent scores and tracks performance by topic. ### 4. Focus Weak Areas Agent identifies gaps, generates targeted practice. --- ## Question Types | Type | Format | Best For | |------|--------|----------| | Multiple choice | 4 options, 1 correct | Quick assessment, certifications | | Multiple select | N options, M correct | Complex topics | | True/False | Statement + T/F | Fast review | | Short answer | 1-3 sentences | Definitions, explanations | | Fill blank | Sentence with ___ | Terminology | | Matching | Connect pairs | Relationships | | Essay | Open response | Deep understanding | --- ## Generating Questions **From notes:** ``` User: "Generate 10 questions from these AWS S3 notes" Agent: Creates mix of types, varying difficulty ``` **By topic:** ``` User: "5 hard questions on database normalization" Agent: Generates challenging application questions ``` **Exam style:** ``` User: "Make questions like the PMP exam" Agent: Matches official format, question style, difficulty ``` --- ## Practice Session ``` 📝 Practice: AWS S3 (10 questions) Q1/10 [Medium] Which S3 storage class has the lowest cost for infrequently accessed data with millisecond retrieval? A) S3 Standard B) S3 Intelligent-Tiering C) S3 Standard-IA ✓ D) S3 Glacier Your answer: _ ``` After answer: ``` ✅ Correct! S3 Standard-IA is designed for infrequently accessed data but requires rapid access when needed. Glacier has lower cost but retrieval takes minutes to hours. [Next] [Skip] [End session] ``` --- ## Storage ``` ~/exams/ ├── {subject}/ │ ├── questions.jsonl # Question bank │ ├── sessions.jsonl # Practice history │ ├── performance.json # Stats by topic │ └── flashcards.json # Generated cards ``` --- ## Study Planning ``` "Create a study schedule — exam in 2 weeks, 3 hours/day available" "Summarize chapter 5 focusing on what's likely to be on the exam" "Make a concept map for [topic]" "Generate a 1-page quick review sheet for [subject]" "Remind me to study at 7pm daily" (uses cron) ``` --- ## Commands ``` "Generate 20 questions from [material]" "Quiz me on [topic]" "Start a timed simulation (50 questions, 60 minutes)" "Show my weak areas" "Create flashcards for [topic]" "Review mistakes from last session" "Grade my essay answer and suggest improvements" ``` --- ### Active Subjects <!-- Subjects being studied --> ### Performance Summary <!-- Overall stats and trends --> ### Focus Areas <!-- Topics needing more practice -->

path: flashcards.md
content: # Flashcards

Spaced repetition for memorization.

---

## Generating Cards

From source material, extract:
- **Term → Definition**
- **Concept → Explanation**
- **Question → Answer**
- **Acronym → Expansion**
- **Process → Steps**

### Card Format
```json
{
  "front": "What is the CAP theorem?",
  "back": "In distributed systems, you can only guarantee 2 of 3: Consistency, Availability, Partition tolerance",
  "topic": "distributed-systems",
  "tags": ["theory", "tradeoffs"]
}
```

---

## Card Types

### Basic (Front/Back)
```
Front: Term or question
Back: Definition or answer
```

### Cloze (Fill in blank)
```
"The {{c1::CAP theorem}} states that distributed systems 
can only guarantee {{c2::two of three}} properties."
```

### Reversed
```
Front: Definition
Back: What term does this describe?
```

### Image Occlusion
```
Front: Diagram with part hidden
Back: Full diagram with labels
```

---

## Spaced Repetition

### Algorithm (SM-2 simplified)
After each review, user rates:
- **Again (1)** — Reset interval to 1 day
- **Hard (2)** — Interval × 1.2
- **Good (3)** — Interval × 2.5
- **Easy (4)** — Interval × 3.0

### Intervals
| Rating | Next Review |
|--------|-------------|
| New card | 1 day |
| Again | 1 day |
| Hard | Previous × 1.2 |
| Good | Previous × 2.5 |
| Easy | Previous × 3.0 |

### Daily Queue
1. Due cards first (overdue prioritized)
2. New cards (limit per day)
3. Review cards (oldest first)

---

## Session Flow

```
📚 Flashcards: AWS Services (23 due)

━━━━━━━━━━━━━━━━━━━━━━━━━━━
What service provides managed 
Kubernetes?
━━━━━━━━━━━━━━━━━━━━━━━━━━━

[Show Answer]
```

After reveal:
```
━━━━━━━━━━━━━━━━━━━━━━━━━━━
Answer: Amazon EKS 
(Elastic Kubernetes Service)
━━━━━━━━━━━━━━━━━━━━━━━━━━━

How well did you know this?

[Again] [Hard] [Good] [Easy]
```

---

## Storage

```json
{
  "cards": [...],
  "reviews": [
    {"card_id": "c001", "date": "2024-02-13", "rating": 3, "interval": 4}
  ],
  "stats": {
    "total": 150,
    "mature": 89,
    "learning": 45,
    "new": 16
  }
}
```

---

## Best Practices

**Creating cards:**
- One fact per card
- Keep fronts short
- Avoid yes/no questions
- Include context when needed
- Add images for visual concepts

**Reviewing:**
- Daily sessions (even 5 minutes)
- Be honest with ratings
- Don't over-add new cards
- Review weak topics more

**Maintenance:**
- Delete cards you've mastered
- Update outdated information
- Merge similar cards
- Tag for easy filtering


path: questions.md
content: # Question Generation

How to create effective practice questions.

---

## From Source Material

### Process
1. User provides content (notes, docs, slides)
2. Extract key concepts, facts, relationships
3. Generate questions at multiple difficulty levels
4. Include distractors (wrong answers) that test understanding

### Extraction Targets
- **Definitions** → What is X?
- **Facts** → When/where/who?
- **Processes** → Steps, sequences
- **Comparisons** → Differences between X and Y
- **Applications** → When would you use X?
- **Relationships** → How does X affect Y?

---

## Question Patterns

### Multiple Choice
```
[Difficulty: Medium]
Question: {Clear, unambiguous question}

A) {Plausible distractor}
B) {Correct answer}
C) {Common misconception}
D) {Partially correct}

Answer: B
Explanation: {Why B is correct, why others are wrong}
```

**Distractor quality matters:**
- Should be plausible to someone who doesn't know
- Test common misconceptions
- Similar length to correct answer
- No obvious tells ("always", "never")

### Short Answer
```
[Difficulty: Medium]
Question: Explain the difference between TCP and UDP.

Expected: {Key points that should be covered}
- TCP is connection-oriented, UDP is connectionless
- TCP guarantees delivery, UDP does not
- TCP has higher overhead

Scoring: {Partial credit criteria}
```

### Scenario-Based
```
[Difficulty: Hard]
Scenario: A company needs to store 10TB of log files that 
are accessed once per month for compliance audits.

Question: Which storage solution minimizes cost while 
meeting access requirements?

A) S3 Standard
B) S3 Standard-IA
C) S3 Glacier Flexible Retrieval
D) S3 Glacier Deep Archive

Answer: C
Explanation: Monthly access rules out Deep Archive (12h+ retrieval).
Glacier Flexible allows hours retrieval at lower cost than IA.
```

---

## Difficulty Calibration

| Level | Cognitive Skill | Example |
|-------|-----------------|---------|
| Easy | Remember, Define | "What does ACID stand for?" |
| Medium | Apply, Analyze | "Which isolation level prevents phantom reads?" |
| Hard | Evaluate, Create | "Design a schema that balances normalization with query performance for..." |

**Distribution for practice:**
- First session: 40% easy, 40% medium, 20% hard
- After basics: 20% easy, 50% medium, 30% hard
- Pre-exam: 10% easy, 40% medium, 50% hard

---

## Avoiding Bad Questions

❌ **Ambiguous:** "Describe databases" (too vague)
❌ **Trivial:** "Is SQL a language?" (yes/no, no learning)
❌ **Trick questions:** Testing attention, not knowledge
❌ **Double negatives:** "Which is NOT incorrect?"
❌ **All of the above:** Lazy question design

✅ **Good questions:**
- One clear correct answer
- Test understanding, not memorization
- Plausible distractors
- Appropriate difficulty
- Actionable feedback on wrong answers

---

## Question Bank Management

**Storage format (questions.jsonl):**
```json
{"id": "q001", "topic": "s3", "type": "mc", "difficulty": "medium", "question": "...", "options": [...], "answer": "B", "explanation": "..."}
{"id": "q002", "topic": "ec2", "type": "short", "difficulty": "easy", "question": "...", "expected": [...]}
```

**Avoiding repeats:**
- Track questions shown in sessions.jsonl
- Rotate through bank before repeating
- Prioritize questions user got wrong


path: simulations.md
content: # Timed Simulations

Mock exams that match real test conditions.

---

## Setting Up a Simulation

User specifies:
- **Question count** — Match real exam (e.g., 65 questions)
- **Time limit** — Match real exam (e.g., 90 minutes)
- **Question types** — Distribution (e.g., 80% MC, 20% scenario)
- **Passing score** — Target percentage (e.g., 72%)

```
"Start AWS SAA simulation: 65 questions, 130 minutes"
```

---

## During Simulation

### Timer Display
```
⏱️ Time: 1:45:32 remaining
📊 Progress: 23/65 questions
```

### Question Display
```
Question 23 of 65 [Flagged: 3]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[Question text...]

A) Option A
B) Option B
C) Option C
D) Option D

[Flag] [Previous] [Next] [Submit All]
```

### Controls
- **Flag** — Mark for review
- **Previous/Next** — Navigate
- **Submit All** — End exam early

---

## End of Simulation

### Results Summary
```
📊 Simulation Complete: AWS SAA

Score: 52/65 (80%) ✅ PASS
Time used: 1:58:42 / 2:10:00
Passing: 72%

By Domain:
━━━━━━━━━━━━━━━━━━━━━━━━━━━
Design Resilient:     18/22 (82%) ✅
Design Performant:    14/18 (78%) ✅
Design Secure:        12/15 (80%) ✅
Design Cost-Optimized: 8/10 (80%) ✅

Flagged questions: 3 (2 correct, 1 wrong)

[Review Wrong] [Review All] [Retake]
```

---

## Review Mode

After simulation, review each question:

```
Question 15 ❌ INCORRECT

Your answer: B
Correct answer: C

[Full question and options displayed]

Explanation:
[Why C is correct and B is wrong]

Related topics: VPC, Security Groups

[Previous] [Next] [Back to Summary]
```

---

## Simulation Types

### Full Mock
- Complete exam replica
- Real time pressure
- No hints or feedback until end

### Practice Mode
- Same questions
- Immediate feedback after each
- Explanations shown
- No time pressure (optional)

### Quick Quiz
- 10-20 questions
- Focused on specific topic
- Timed or untimed

### Review Weak Areas
- Questions from topics with low scores
- Adaptive difficulty

---

## Matching Real Exams

| Exam | Questions | Time | Format |
|------|-----------|------|--------|
| AWS SAA | 65 | 130 min | MC, Multiple select |
| AWS SAP | 75 | 180 min | MC, Multiple select |
| PMP | 180 | 230 min | MC |
| CCNA | 100-120 | 120 min | MC, Drag-drop |

When user says exam name, match format automatically.

---

## Performance Over Time

Track simulation scores:
```json
{
  "simulations": [
    {"date": "2024-02-10", "score": 68, "time": 125},
    {"date": "2024-02-12", "score": 74, "time": 118},
    {"date": "2024-02-13", "score": 80, "time": 112}
  ]
}
```

Show trend:
```
📈 Simulation History

Feb 10: 68% (125 min) — needs improvement
Feb 12: 74% (118 min) — getting there
Feb 13: 80% (112 min) — ready! ✅

Trend: ↑12% improvement, ↓13 min faster
```


path: tracking.md
content: # Performance Tracking

Monitor progress and identify weak areas.

---

## Metrics Tracked

### Per Question
- Correct/incorrect
- Time spent
- Topic/domain
- Difficulty level
- Date attempted

### Per Topic
- Total questions attempted
- Accuracy percentage
- Trend over time
- Time per question average

### Overall
- Questions completed
- Overall accuracy
- Simulation scores
- Study streak

---

## Identifying Weak Areas

### By Accuracy
```
📊 Topic Performance

✅ Strong (>80%):
   EC2: 92% (24/26)
   S3: 88% (29/33)

⚠️ Needs work (60-80%):
   VPC: 71% (17/24)
   IAM: 68% (15/22)

❌ Weak (<60%):
   Lambda: 52% (11/21)
   DynamoDB: 48% (10/21)
```

### By Recency
Topics not practiced recently:
```
⏰ Stale Topics (no practice in 7+ days):
   - CloudFormation (12 days)
   - Route 53 (9 days)
   - ELB (8 days)
```

---

## Gap Analysis

Before exam, generate report:

```
📋 Readiness Report: AWS SAA

Overall: 76% ready

By Domain:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Design Resilient    ████████░░ 82%
Design Performant   ███████░░░ 71%
Design Secure       ████████░░ 78%
Cost-Optimized      ██████░░░░ 64% ⚠️

Priority Focus:
1. DynamoDB (48%) — 15 more questions recommended
2. Cost Optimization (64%) — 10 more questions
3. Lambda (52%) — 12 more questions

Estimated study time: 4-6 hours
```

---

## Adaptive Practice

Based on performance, agent adjusts:

### Question Selection
- 40% from weak topics
- 30% from medium topics
- 20% from strong topics (maintenance)
- 10% new topics

### Difficulty Adjustment
- Struggling with medium? Add more easy first
- Acing hard? Ready for simulation

---

## Progress Reports

### Daily
```
📊 Today's Practice

Questions: 25
Accuracy: 76%
Time: 32 minutes
Topics: VPC, IAM, Lambda

Streak: 5 days 🔥
```

### Weekly
```
📊 Week Summary (Feb 5-11)

Total questions: 142
Average accuracy: 74% (↑6%)
Time invested: 3.2 hours
Simulations: 2 (avg: 71%)

Top improvement: Lambda +18%
Needs attention: DynamoDB (dropped 5%)

Next week focus: DynamoDB, Cost Optimization
```

---

## Storage Format

**performance.json:**
```json
{
  "updated": "2024-02-13T10:00:00Z",
  "topics": {
    "ec2": {"attempts": 26, "correct": 24, "avg_time": 45},
    "lambda": {"attempts": 21, "correct": 11, "avg_time": 62}
  },
  "overall": {
    "total": 245,
    "correct": 186,
    "accuracy": 0.76
  },
  "streak": {
    "current": 5,
    "longest": 12
  }
}
```

---

## Motivation Features

### Streaks
```
🔥 5 day streak! Keep it up!
```

### Milestones
```
🎯 Achievement: 500 questions completed!
📈 Personal best: 85% on simulation!
```

### Recommendations
```
💡 You're ready for a simulation!
   Last 3 days: 78% accuracy
   Target: 72% passing
```


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