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name: mit-exam-generator description: | Generate rigorous MIT PhD-level qualifying examinations from Markdown/Obsidian notes. This skill should be used when users request quiz creation, exam generation, assessment materials, practice questions, or study guides. Triggers on "create quiz", "generate exam", "make practice questions", "assessment", "test me on", or any request for educational testing materials from source content.

MIT PhD Qualifying Exam Generator

Generate rigorous academic assessments from structured Markdown content.

What This Skill Does

• Generates 200-question PhD qualifying exams from Markdown/Obsidian notes
• Scales question count proportionally for sparse content
• Auto-detects difficulty from content complexity
• Merges multiple source documents with weighted question distribution
• Validates answer distribution, difficulty spread, and source coverage

What This Skill Does NOT Do

• Process PDFs, images, or non-Markdown formats
• Generate answer explanations with direct quotes (exam integrity)
• Create exams from external web content
• Provide answer keys without completing the full exam

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Required Clarifications

Before generating, clarify with user:

Optional Clarifications

Ask only if relevant:

• Custom question count override?
• Specific sections to emphasize or exclude?
• Target audience adjustment (undergrad vs PhD)?

If User Doesn't Respond

Use defaults and note assumptions in exam header:

Copy**Assumptions:** Merged thematically, balanced difficulty, standard timing

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Before Implementation

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Exam Specifications

Grading Scale

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Generation Workflow

Copy1. ANALYZE
   └── Read source files → Extract concepts → Map sections
   └── Calculate: content_density = concepts / sections

2. CALIBRATE
   └── question_count = min(200, concepts * 2)
   └── difficulty_profile = analyze_complexity(content)

3. DISTRIBUTE
   └── Allocate questions by type (see references/question-patterns.md)
   └── Allocate by Bloom's level (see references/bloom-taxonomy.md)
   └── Weight by source document size (multi-doc)

4. GENERATE
   └── Create questions following type patterns
   └── Ensure distractors are plausible (70-90% correct)
   └── Track source section for each question

5. VALIDATE
   └── Run all checks (see references/validation-rules.md)
   └── Fix any failures before delivery

6. OUTPUT
   └── Save to exam-[source-name].md alongside source

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Question Type Distribution

See references/question-patterns.md for templates and examples.

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Bloom's Taxonomy Distribution

See references/bloom-taxonomy.md for level indicators.

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Answer Construction Rules

1. Option A: Never "All/None of the above"
2. Correct Answer: One clearly correct option
3. Distractors: Plausible but fail on critical detail (70-90% correct)
4. Distribution: Roughly equal A:B:C:D across exam
5. Sequences: No more than 3 consecutive same-letter answers

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Multi-Document Handling

When multiple source files provided:

Copyweight[doc] = word_count[doc] / total_word_count
questions[doc] = round(total_questions * weight[doc])

Create distinct sections per source or merge thematically (user preference).

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Output Format

Copy# [Exam Title]
## MIT PhD Qualifying Examination

**Source:** [file(s)]
**Questions:** [N]
**Duration:** [X] minutes
**Generated:** [date]

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### PART A: [Topic] ([X] Questions)

**Q1.** [Question stem]
A) [Option]
B) [Option]
C) [Option]
D) [Option]

[Continue all questions...]

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## ANSWER KEY

| Q# | Ans | Section | Difficulty | Bloom |
|----|-----|---------|------------|-------|
| 1 | C | Part A | Medium | Apply |

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## EXPLANATIONS

### Q1
**Correct: C**
[Explanation with section reference - NO direct quotes]
Section: [Heading from source]

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Scaling Algorithm

Copydef calculate_questions(content):
    concepts = extract_testable_concepts(content)

    if len(concepts) >= 100:
        return 200  # Full exam
    elif len(concepts) >= 50:
        return 100  # Half exam
    elif len(concepts) >= 25:
        return 50   # Quarter exam
    else:
        return max(25, len(concepts))  # Minimum viable

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Edge Case Handling

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Validation Pipeline

Run ALL checks before delivery. See references/validation-rules.md.

Quick Checklist

• Question count matches calculated target
• Each question has exactly 4 options (A-D)
• Answer distribution within 20-30% per letter
• No >3 consecutive same-letter answers
• All Bloom levels represented per distribution
• All question types represented per distribution
• Every question has section reference
• No direct quotes in explanations
• Difficulty distribution matches content complexity

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Reference Files