---
name: learn-quiz
description: Test understanding of recently written or accepted code
argument-hint: "[topic] [--hard]"
effort: low
---

# Quiz me

Test understanding of recently written or accepted code.

## Usage

```
/learn:quiz                     # Quiz on last code worked with
/learn:quiz error handling      # Focus on specific aspect
/learn:quiz --hard              # More challenging questions
```

## Instructions

1. Identify the last code I worked with (wrote, edited, or accepted from AI)
2. Generate 3-5 questions that test understanding at different levels:
   - **Recall**: What does this code do?
   - **Understanding**: Why was this approach chosen?
   - **Application**: What would happen if X changed?
   - **Analysis**: What are the trade-offs of this approach?
   - **Synthesis**: How would you extend this?
3. Present questions one at a time
4. Wait for my answer before revealing the correct response
5. Provide explanations with each answer, not just "correct/incorrect"

## Question types

### Level 1: Recall
- "What does the function X return?"
- "What parameters does Y accept?"
- "What happens when Z is called?"

### Level 2: Understanding
- "Why do we use X instead of Y here?"
- "What problem does this pattern solve?"
- "Why is this line necessary?"

### Level 3: Application
- "What would happen if we removed line X?"
- "How would you add feature Y to this code?"
- "What would break if input Z was provided?"

### Level 4: Analysis
- "What are the performance implications of this approach?"
- "What edge cases might cause issues?"
- "How does this compare to alternative X?"

### Level 5: Synthesis
- "How would you refactor this for better testability?"
- "What would need to change to support X?"
- "Design an extension that adds Y"

## Focus areas

When focus is specified (e.g., `/learn:quiz error handling`), prioritize questions about:

| Focus | Question Themes |
|-------|-----------------|
| `error handling` | Try/catch, error types, recovery strategies |
| `performance` | Big-O, optimization, bottlenecks |
| `security` | Input validation, XSS, injection |
| `testing` | Test cases, edge cases, mocking |
| `architecture` | Patterns, separation of concerns, SOLID |
| `types` | TypeScript types, inference, generics |

## Difficulty modes

### Default
- 3 questions
- Mix of Level 1-3
- Focus on understanding current code

### `--hard`
- 5 questions
- Levels 3-5
- Include hypothetical modifications
- Ask about trade-offs and alternatives

## Response format

For each question:

```
## Question 1 of 3

[Question text]

What's your answer?

---
(After user responds)

### Feedback

[Whether correct and why]

**Key insight**: [The concept this tests]

**Related concept**: [Something to explore further]

Ready for the next question?
```

## After quiz complete

Summarize:
- Score: X/Y correct
- Strengths: [Topics understood well]
- Review needed: [Topics to revisit]
- Suggested practice: [Specific exercise]

## Example session

```
User: /learn:quiz

Claude: Looking at your last code - the useEffect hook for data fetching.

## Question 1 of 3

In your useEffect, you have an empty dependency array [].
What does this mean for when the effect runs?

User: It runs only once when the component mounts

Claude: ### Feedback
Correct! An empty dependency array means the effect runs only on mount
(and cleanup on unmount).

**Key insight**: The dependency array controls WHEN effects re-run.

**Related concept**: What happens with no array vs. [someValue]?

Ready for Question 2?
```

## Tips for users

1. **Be honest**: Wrong answers are learning opportunities
2. **Explain your reasoning**: Helps identify gaps even in correct answers
3. **Ask follow-ups**: If feedback is unclear, dig deeper
4. **Track weak areas**: Note topics that need more practice

$ARGUMENTS


---
Source attribution: FlorianBruniaux
Original project: https://github.com/FlorianBruniaux/claude-code-ultimate-guide
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: FlorianBruniaux
Original project: https://github.com/FlorianBruniaux/claude-code-ultimate-guide
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Content & Media, Design, Education & Research, Sales
Roles: Customer Success Manager, Sales Development Rep (SDR), Technical Writer, Tutor / Instructor, UX Writer
Specialties: Design, Frontend, Testing
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 2e982708df21bafc734fc3000c420c68091f43714ae3c4f6be857ad3efc641a5