Academic Review Skill

Overview

This skill enables interactive review sessions with academic PDFs while preserving your privacy. All PDF processing happens locally on your machine using the Marker library - PDFs are never sent to Anthropic servers. Only extracted text (with LaTeX formulas) is used in our conversation.

Supported Document Types:

Lecture Slides: Review presentations, generate quizzes, Q&A on concepts
Research Papers: Analyze methodology, results, and discussion sections
Book Chapters: Study concepts, work through examples and exercises

Key Features:

Privacy-preserving: PDFs processed locally, never uploaded
Math-aware: Formulas preserved in LaTeX format (e.g., $E = mc^2$ , $$\int_a^b f(x)dx$$)
Cached extraction: First extraction is slow, subsequent access is instant
Two review modes: Q&A (free-form questions) and Quiz (auto-generated questions with scoring)
Visual fallback: Can extract specific pages as images for complex diagrams

Document Type Detection

When starting a review session, identify the document type from context:

Lectures - Indicators:

File names with "lecture", "slides", "presentation"
Bullet-point heavy content
Sequential slide numbers
Course/semester codes (e.g., "CS229_Lecture05.pdf")

Research Papers - Indicators:

File names with "paper", author names, conference/journal codes
Standard sections: Abstract, Introduction, Methods, Results, Discussion, References
Citations and bibliography
Two-column format common

Book Chapters - Indicators:

File names with "chapter", book titles
Sections and subsections with numbered headings
End-of-chapter exercises or problems
Dense paragraph-based text

Default approach: If unclear, start with Q&A mode and adapt based on the content structure.

Quick Start

Starting a Review Session

When the user requests a review session:

Find PDFs using Glob: **/*.pdf or more specific patterns
Identify document type (lecture/paper/chapter) from filename and request
Extract content using the extraction script
Ask mode preference: "Would you like Q&A mode or Quiz mode?"
Begin the selected mode with document-type-appropriate approach

Example Flow

User: "Review the SLAM paper by Smith et al."

Your actions:
1. Use Glob to find PDFs matching "smith" or "slam"
2. Identify as research paper
3. Run: python scripts/extract_pdf.py <pdf_path>
4. Read the cached markdown file
5. Ask: "Q&A mode or Quiz mode?"
6. Begin selected mode (adapt to paper structure)

Review Modes

Q&A Mode (Free-Form Questions)

Purpose: Answer user's specific questions about content with detailed explanations.

How to conduct Q&A mode:

Load and parse content:

# Extract PDF (or use cached version)
python scripts/extract_pdf.py /path/to/document.pdf

Then read the output markdown file using the Read tool.

Present overview (adapt to document type):

For Lectures:

List main topics covered
Highlight key formulas (show in LaTeX)
Mention important definitions or concepts

Example:

📚 Lecture Overview: Epipolar Geometry

This lecture covers 45 slides on:
- Epipolar constraint: $x'^T F x = 0$
- Fundamental matrix $F$ (3x3, rank 2)
- Essential matrix $E = K'^T F K$
- Applications: stereo vision, 3D reconstruction

Ask me anything about these topics, or say "quiz" to switch to quiz mode.

For Research Papers:

Summarize the research question/contribution
Key methodology and approach
Main results and conclusions
Important formulas or algorithms

Example:

📄 Paper Overview: "ORB-SLAM2: Real-Time SLAM for Monocular, Stereo and RGB-D Cameras"

**Research Question**: How to build a complete SLAM system that works across multiple camera types?

**Key Contributions**:
- Unified SLAM system for monocular, stereo, and RGB-D cameras
- Place recognition and loop closing
- Real-time performance on standard CPUs

**Methods**: ORB features, bag-of-words place recognition, pose graph optimization

**Results**: Evaluated on KITTI and TUM datasets, outperforms previous methods

Ask me about methodology, results, or implementation details.

For Book Chapters:

Main concepts introduced
Theorems or key results
Important formulas
Example problems covered

Example:

📖 Chapter Overview: "Matrix Decompositions" (Chapter 7)

**Topics Covered**:
- Singular Value Decomposition (SVD): $A = U\Sigma V^T$
- Eigenvalue decomposition: $A = Q\Lambda Q^T$
- QR decomposition and applications
- Least squares via matrix decompositions

**Key Theorems**: Spectral theorem, SVD existence

**Exercises**: 15 problems on computing decompositions and applications

Ask me about concepts, work through examples, or get help with exercises.

Answer questions:
- Reference specific page/section numbers
- Show formulas in LaTeX format
- Explain concepts with examples
- Connect related topics
- If user asks about a diagram, offer to extract that page as an image
Track progress:
- Note which topics user asks about
- Identify apparent knowledge gaps
- Suggest related concepts proactively

Document-specific guidance:

Lectures: Focus on concept understanding, derivations, applications Papers: Focus on methodology critique, results interpretation, reproducibility Chapters: Focus on theorem understanding, example walkthrough, exercise solving

Quiz Mode (Auto-Generated Questions)

Purpose: Test user's knowledge with auto-generated questions, provide scoring and feedback.

How to conduct Quiz mode:

Load and analyze content:
```
# Extract PDF (or use cached version)
python scripts/extract_pdf.py /path/to/document.pdf
```
Read the markdown and analyze:
- Key concepts and definitions
- Important formulas (in LaTeX)
- Learning objectives
- Example problems
Generate questions (default: 10, but ask user for preference):

For Lectures:
- Multiple choice: Test understanding of concepts
- True/False: Quick concept checks
- Short answer: Define terms or explain relationships
- Formula problems: Apply equations to scenarios
For Research Papers:
- Multiple choice: Methodology choices, experimental design
- True/False: Claims about results or methods
- Short answer: Explain key contributions, limitations
- Analysis questions: Critique methods or interpret results
For Book Chapters:
- Multiple choice: Theorem conditions, concept understanding
- True/False: Mathematical statements
- Short answer: Prove simple results, explain concepts
- Problems: Similar to end-of-chapter exercises
Mix question types and topics proportionally. Order by difficulty (easier first).

Present questions one at a time:

Quiz Mode - 10 Questions
Score: 0/0

Question 1 of 10 [Multiple Choice]
What is the rank of the Fundamental matrix $F$?

a) 1
b) 2
c) 3
d) 4

Your answer:

Evaluate and provide feedback:

✓ Correct! [+1 point]

The Fundamental matrix $F$ has rank 2, which means det($F$) = 0. This constraint
arises from the fact that $F$ maps points to epipolar lines, and the mapping has
a one-dimensional null space.

Score: 1/1 (100%)

Question 2 of 10...

For incorrect answers:

✗ Incorrect [+0 points]
Your answer: a) 1
Correct answer: b) 2

The Fundamental matrix has rank 2, not 1. The rank-2 constraint (det($F$) = 0)
is one of the key properties used in estimating $F$ from point correspondences.

Score: 1/2 (50%)

Question 3 of 10...

End with summary:

📊 Quiz Complete!

Final Score: 8/10 (80%) - B

✓ Topics Mastered:
- Epipolar constraint
- Essential matrix properties
- Stereo reconstruction basics

⚠️ Topics to Review:
- Fundamental matrix estimation (8-point algorithm)
- RANSAC for outlier rejection

Would you like to:
1. Review the topics you missed in Q&A mode?
2. Take another quiz on the same material?
3. Move to a different document?

Scoring Guidelines:

Multiple choice: 1 point for correct answer
True/False: 1 point for correct answer
Short answer: 1 point if answer captures key concept (be flexible)
Formula problems: 1 point for correct answer, 0.5 for correct approach but calculation error

Quiz Logging

IMPORTANT: At the end of every quiz, automatically create a log file without prompting.

File location: .cache/quiz-logs/YYYY-MM-DD_pcv{N}_{topic}.md

Example: .cache/quiz-logs/2026-01-13_pcv05_DLT.md

During the quiz, track:

Which slide/page each question sources from (use _page_N_ markers in extracted content)
Student's reasoning when provided
Specific knowledge gaps revealed by incorrect answers

Log file structure:

YAML frontmatter: date, lecture number, topic, source PDF path, cache hash, score, percentage
Performance summary table: score, grade, date
Mastered topics table: topic, question numbers, slide references, notes on understanding
Topics needing review table: topic, question numbers, slide references, specific knowledge gap
Question-by-question detail: Full detail for each question, especially reasoning and gaps for incorrect answers
Study recommendations: Prioritized list for next session
Machine-readable metadata block: YAML block with topics_covered, weak_topics, strong_topics arrays

Slide reference format:

Extract page number from _page_N_ markers in the cached markdown
Reference as "Slide N" or "Slides N-M" for ranges
Include content description (e.g., "Slide 3, transformation DOF table")

Example log file:

---
date: 2026-01-13
lecture: pcv05
topic: Direct Linear Transformation (DLT)
source_pdf: lectures/PCV/pcv05_WS2526_DLT.pdf
cache_hash: c57edf3d7baf5f7d
score: 9/10
percentage: 90
grade: A
---

# Quiz Log: PCV Lecture 5 - DLT

## Performance Summary
| Metric | Value |
|--------|-------|
| Score | 9/10 (90%) |
| Grade | A |
| Date | 2026-01-13 |

## Topics Assessed

### Mastered Topics
| Topic | Questions | Slide References | Notes |
|-------|-----------|------------------|-------|
| Degrees of freedom | Q1 | Slide 3 (transformation table) | Understood 8 DOF = 9 elements - 1 scale |
| Point requirements | Q2 | Slides 16-17 | 4 points for 2D homography |

### Topics Needing Review
| Topic | Questions | Slide References | Specific Gap |
|-------|-----------|------------------|--------------|
| Conic transformation | Q8 | Slide 7 | Formula: C' = H^{-T}CH^{-1} |

## Question-by-Question Detail

### Q1: Degrees of Freedom [CORRECT]
- **Topic**: 2D transformation properties
- **Slide**: 3
- **Answer**: c) 8
- **Student reasoning**: "3x3 minus one for scaling"
- **Assessment**: Full understanding demonstrated

### Q8: Conic Transformation [INCORRECT]
- **Topic**: Transformation of geometric primitives
- **Slide**: 7
- **Correct answer**: C' = H^{-T}CH^{-1}
- **Student response**: "I'm not sure how conics are transformed"
- **Recommended review**: Review derivation from point incidence x^T C x = 0

## Study Recommendations for Next Session
1. **Priority**: Conic transformation formula and derivation
2. **Related topics to reinforce**: Dual conics (C*' = HC*H^T)

## Metadata for Future Processing
```yaml
session_type: quiz
question_count: 10
correct_count: 9
topics_covered:
  - degrees_of_freedom
  - point_requirements
  - conic_transformation
weak_topics:
  - conic_transformation
strong_topics:
  - degrees_of_freedom
  - point_requirements


**Purpose**: These logs enable future Claude instances to:
- Identify persistent weak areas across multiple sessions
- Recommend focused review before exams
- Track learning velocity and mastery progression
- Generate personalized study plans based on history

## Web Quiz System

The web quiz system provides a question pool organized by exam topics. Questions are designed for mastery-focused learning, not slide memorization.

### Architecture Overview

LOCAL SERVER MOBILE ───────────────────────────────────────────────────────────────── Claude generates rsync Flask serves Safari question files ──────────▶ quizzes ◀─────────── iPhone │ (+ offline) server/data/questions/ │ ▼ Results saved as JSON files


### Question Pool Structure

Questions are organized by exam topic in `server/data/questions/`:

server/data/questions/ homogeneous_2d.json # Points, lines, dual conics (pcv3, pcv4) transformations.json # Planar transforms, DLT (pcv4-6) homogeneous_3d.json # 3D coords, quadrics (pcv7) camera_model.json # Projection matrix, orientation (pcv8, pcv9) distortion.json # Radial distortion, aberrations (pcv10) epipolar.json # F matrix, E matrix, 8-point (pcv11, pcv12) triangulation.json # Intersection, stereo, reconstruction (pcv13) trifocal.json # Trifocal tensor (pcv14, pcv15) bundle_adjustment.json # Nonlinear optimization (pcv17, pcv19) calibration.json # Self-calibration, DAQ (pcv18, pcv20) robust_estimation.json # RANSAC, M-estimators (pcv21)


Each file contains an array of questions (no quiz metadata needed).

### Question JSON Format

```json
[
  {
    "id": "hom2d_001",
    "type": "multiple_choice",
    "question": "The fundamental matrix $F$ has rank 2. This means $\\det(F) = 0$, which provides one constraint when estimating $F$ from point correspondences. How many independent parameters does $F$ have after accounting for this rank constraint and overall scale ambiguity?",
    "options": ["6", "7", "8", "9"],
    "correct": 1,
    "topic": "fundamental_matrix_properties"
  },
  {
    "id": "hom2d_002",
    "type": "true_false",
    "question": "Given a homography $H$ that maps points as $x' = Hx$, lines are transformed by the same matrix: $l' = Hl$.",
    "correct": false,
    "topic": "line_transformation"
  }
]

Critical Question Quality Guidelines

1. SELF-CONTAINED QUESTIONS

Every question must be answerable WITHOUT referencing slides. Include all necessary context:

BAD (requires slide knowledge):

"question": "What is the rank of F?"

GOOD (self-contained):

"question": "The fundamental matrix $F$ encodes the epipolar geometry between two views. Given that $F$ maps points to epipolar lines via $l' = Fx$, and the epipole $e'$ lies on all epipolar lines (so $Fe = 0$), what is the rank of $F$?"

2. USE PROPER LATEX NOTATION

The web UI renders LaTeX via KaTeX. Always use proper notation:

Inline math: $F$ , $x'^T F x = 0$
Display math: $$P = K[R|t]$$
Common symbols: $\\mathbf{x}$ , $\\lambda$ , $\\Sigma$
Matrices: $\\begin{bmatrix} a & b \\\\ c & d \\end{bmatrix}$

3. NO SHORT_ANSWER TYPE FOR WEB QUIZZES

Only use multiple_choice and true_false for web quizzes. Short answer questions with keyword matching don't work well on mobile and don't test understanding effectively.

4. FOCUS ON UNDERSTANDING, NOT MEMORIZATION

BAD (trivia/memorization):

"question": "Who introduced the 8-point algorithm?"

GOOD (tests understanding):

"question": "The 8-point algorithm estimates the fundamental matrix $F$ from point correspondences. Why is normalization of point coordinates crucial before applying SVD to solve the linear system?"

5. USE EXTERNAL KNOWLEDGE FREELY

Questions can and should draw from:

Hartley & Zisserman "Multiple View Geometry"
Faugeras "Three-Dimensional Computer Vision"
Standard photogrammetry literature

Don't limit questions to what's explicitly on slides - exam questions test understanding of the field, not slide memorization.

6. RANDOMIZE ANSWER POSITIONS

Use scripts/quiz_utils.py to avoid position bias:

from scripts.quiz_utils import create_mc_question

question = create_mc_question(
    question="How many DOF does a 2D homography have?",
    correct="8",
    distractors=["4", "6", "9"],
    id="trans_001",
    topic="homography_dof"
)

Topic-Specific Guidance

Epipolar Geometry (highest weight on exam)

Essential vs fundamental matrix properties and relationship
8-point algorithm derivation and why normalization matters
Epipolar constraint geometric interpretation
Degenerate configurations

Camera Model

Projection matrix decomposition $P = K[R|t]$
Interior vs exterior orientation
Principal point, focal length, skew interpretation
DLT for spatial resection

Robust Estimation

RANSAC: why it works, parameter selection, failure modes
M-estimators and influence functions
When to use RANSAC vs M-estimators vs LMedS

Example High-Quality Questions

{
  "id": "epi_001",
  "type": "multiple_choice",
  "question": "The essential matrix $E$ relates corresponding points in normalized image coordinates: $\\hat{x}'^T E \\hat{x} = 0$. Unlike the fundamental matrix $F$, the essential matrix has additional structure because it can be decomposed as $E = [t]_\\times R$. How many degrees of freedom does $E$ have?",
  "options": ["5", "6", "7", "9"],
  "correct": 0,
  "topic": "essential_matrix_dof"
}

{
  "id": "robust_001",
  "type": "true_false",
  "question": "RANSAC is guaranteed to find the optimal solution (maximum inlier set) if given enough iterations. The number of iterations needed depends on the inlier ratio and the number of points needed to fit the model.",
  "correct": false,
  "topic": "ransac_properties"
}

Server Deployment

Questions are stored in server/data/questions/. Deploy to server using rsync:

rsync -avz server/data/questions/ user@server:/var/www/quizzes/data/questions/

The server provides two quiz modes:

Quick Quiz (/quick?count=N): Random N questions from all topics
Topic Practice (/topic/<topic_id>): All questions for a specific topic

Offline Support

The web app supports offline mode via localStorage. Users can:

Click "Download for Offline" on the home page
Take quizzes without network connection
Results are queued locally and synced when back online

Quiz Validation

Before deploying quizzes to the web server, validate them with:

./scripts/validate_quizzes.py

This validates all quiz JSON files in server/data/ against the expected format:

Quiz-level validation:

Required fields: id, lecture, topic, questions
ID matches filename
Valid lecture format

Question-level validation:

Required fields: id, type, question
Valid question types: multiple_choice, true_false, short_answer
Type-specific requirements:
- Multiple choice: options array (2-6 items), correct index in range
- True/false: correct as boolean
- Short answer: expected_keywords array (non-empty)
No duplicate question IDs

Example output:

Quiz Validation Report
==================================================

✓ All 96 quizzes are valid!

Statistics:
  Total quizzes:     96
  Valid quizzes:     96
  Total questions:   960
  Multiple choice:   633 (65.9%)
  True/False:        231 (24.1%)
  Short answer:      96 (10.0%)

Run this after generating or modifying quizzes to catch formatting errors before they cause server issues.

Quiz Status

Check which lectures have quizzes and how many:

./scripts/quiz_status.sh

Shows quiz coverage per lecture with counts and identifies lectures missing quizzes.

Finding PDFs

General patterns:

# All PDFs in current directory and subdirectories
glob pattern: "**/*.pdf"

# Find specific document by name
glob pattern: "**/*smith*.pdf"

# Course-specific (if organized in directories)
glob pattern: "CS229/**/*.pdf"

When user's request is ambiguous:

Use Glob to find matching PDFs
Present options if multiple matches
Let user select which PDF to review

Extraction and Caching

Checking the Cache First

IMPORTANT: Before running the extraction script, always check if the PDF is already cached:

# List all cached metadata files to find source PDFs
for f in .cache/extracted/*.json; do cat "$f" | head -5; done

Or search for a specific PDF:

grep -l "pcv10" .cache/extracted/*.json

Each .json file contains the source PDF path and hash. If found, read the corresponding .md file directly:

# If pcv10 has hash f2c4d6104b10076b:
Read .cache/extracted/f2c4d6104b10076b.md

This avoids unnecessary extraction and speeds up sessions.

First-Time Extraction

When extracting a PDF for the first time (not in cache):

# Run extraction script (uses venv python)
source .venv/bin/activate && python scripts/extract_pdf.py /path/to/document.pdf

This will:

Convert PDF to markdown using Marker
Preserve formulas as LaTeX
Cache result in .cache/extracted/<hash>.md
Save metadata in .cache/extracted/<hash>.json
Take 30-60 seconds depending on PDF size

The script prints the path to the cached markdown file - use Read to load it.

Using Cached Extraction

If the PDF was previously extracted:

Script immediately returns cached file path
Read the markdown file using Read tool
Instant access (no re-processing)

Extracting Page Images (Fallback)

If user asks about a specific diagram or visual element:

# Extract page 23 as an image
source .venv/bin/activate && python scripts/extract_page_image.py /path/to/document.pdf 23

Then use Read to view the image and analyze it visually.

Working with LaTeX Formulas

Extracted formulas are in LaTeX format:

Inline: $E = mc^2$ Display: $$\int_a^b f(x)dx$$

When explaining formulas:

Show the LaTeX notation
Explain each variable
Provide context from the document
Give examples if helpful

Example:

The epipolar constraint is expressed as:

$$x'^T F x = 0$$

Where:
- $x$ and $x'$ are corresponding points in homogeneous coordinates
- $F$ is the 3×3 Fundamental matrix
- The equation states that $x'$ lies on the epipolar line $Fx$ in the second image

Tips for Best Results

General Guidelines:

Always extract first: Run the extraction script before answering questions
Use caching: Check if extraction already exists (script handles this)
Reference pages: Include page numbers when answering
Show formulas: Display LaTeX formulas when explaining math concepts
Be interactive: Ask follow-up questions, offer deeper explanations
Adapt to document type: Use appropriate review style (lectures vs papers vs chapters)

For Math-Heavy Content:

Formulas are preserved in LaTeX - use them!
Explain notation and variables clearly
Show step-by-step derivations when helpful
Offer to extract page images for complex diagrams

For Research Papers:

Focus on understanding methodology and contributions
Help interpret results and figures
Discuss limitations and future work
Compare with related work when relevant

For Book Chapters:

Work through examples step-by-step
Help with end-of-chapter exercises
Connect concepts across chapters
Prove theorems when requested

For Multi-PDF Sessions:

Can review multiple documents in one session
Cross-reference concepts between documents
Build connections across topics

Mode Switching:

User can switch from Q&A to Quiz (or vice versa) anytime
Just ask and switch modes
Keep the extracted content loaded

Session Examples

Lecture Review Session

User: "Quiz me on the SLAM lecture"

Your actions:
1. glob pattern: "**/*slam*.pdf"
2. Find matching PDF (e.g., "Lecture_12_SLAM.pdf")
3. Identify as lecture (filename, slide structure)
4. source .venv/bin/activate && python scripts/extract_pdf.py Lecture_12_SLAM.pdf
5. Read cached markdown
6. Generate 10 questions covering SLAM topics
7. Start quiz mode

Research Paper Review Session

User: "Help me understand the ORB-SLAM2 paper"

Your actions:
1. glob pattern: "**/*orb*slam*.pdf"
2. Find matching PDF
3. Identify as research paper (structure, citations)
4. source .venv/bin/activate && python scripts/extract_pdf.py orb_slam2.pdf
5. Read cached markdown
6. Present paper overview (research question, methods, results)
7. Enter Q&A mode - focus on methodology and results interpretation

Book Chapter Review Session

User: "Review chapter 7 on matrix decompositions"

Your actions:
1. glob pattern: "**/*chapter*7*.pdf" or "**/*matrix*.pdf"
2. Find matching PDF
3. Identify as book chapter (numbered sections, exercises)
4. source .venv/bin/activate && python scripts/extract_pdf.py chapter_07_decompositions.pdf
5. Read cached markdown
6. Present chapter overview (concepts, theorems, exercises)
7. Ask: "Q&A mode or Quiz mode?"
8. If Q&A: Help with concepts and exercises
9. If Quiz: Generate problems similar to exercises

Switching Modes Mid-Session

[In Q&A mode]
User: "Actually, can you quiz me instead?"

Your response:
"Sure! I'll generate a quiz based on this content. How many questions would you like? (default: 10)"

User: "10 is fine"

Your response:
[Generate 10 questions and start quiz mode]

Using Page Images

User: "I don't understand the diagram on page 15"

Your actions:
1. source .venv/bin/activate && python scripts/extract_page_image.py /path/to/document.pdf 15
2. Read the image file
3. Analyze the diagram visually
4. Explain what it shows, referencing specific elements

Your response:
"Let me extract that diagram for you..."
[After reading image]
"This diagram shows the epipolar geometry configuration. I can see two cameras (left and right) viewing a 3D point P. The key elements are:
- Point P in 3D space
- Its projections p and p' in the two images
- The baseline connecting camera centers C and C'
- The epipolar plane (gray triangle)
- Epipolar lines l and l' in each image

Would you like me to explain how these elements relate to the Fundamental matrix?"

Error Handling

If extraction fails:

Check if PDF file exists
Ensure dependencies are installed (pip list | grep marker)
Check file permissions
Report error to user with helpful message

If formula extraction is unclear:

Show what was extracted
Offer to extract the page as an image
Analyze the formula visually from the image

If no PDFs found:

Double-check the glob pattern
Ask user for the PDF file path
Clarify which document they want to review

Privacy Reminder

Always remember: PDFs are processed locally. Only extracted markdown text (with LaTeX formulas) is sent to Claude. The original PDFs never leave the user's machine. This ensures privacy for proprietary or sensitive academic materials.

For detailed documentation, see reference.md. For usage examples, see examples.md.

academic-review

Academic Review Skill

Overview

Document Type Detection

Quick Start

Starting a Review Session

Example Flow

Review Modes

Q&A Mode (Free-Form Questions)

Quiz Mode (Auto-Generated Questions)

Quiz Logging

Critical Question Quality Guidelines

Topic-Specific Guidance

Example High-Quality Questions

Server Deployment

Offline Support

Quiz Validation

Quiz Status

Finding PDFs

Extraction and Caching

Checking the Cache First

First-Time Extraction

Using Cached Extraction

Extracting Page Images (Fallback)

Working with LaTeX Formulas

Tips for Best Results

Session Examples

Lecture Review Session

Research Paper Review Session

Book Chapter Review Session

Switching Modes Mid-Session

Using Page Images

Error Handling

Privacy Reminder