Turn lecture slides into exam-ready study materials.

中文

📌 Finals-season note · 期末季说明 One update is planned for mid-June. I'm using EPA for my own finals right now and will share real usage notes along the way — but I won't have time to answer questions for a while, so issues / DMs may be delayed. Thanks for understanding 🙏 6 月中旬会有一次更新。作者正用 EPA 备考自己的期末，会同步分享实测体验；但近期没精力一一答疑，Issue / 私信会延迟回复，感谢理解 🙏

An AI-powered exam prep assistant. Drop in lecture PPTs, Word handouts, or PDF readings — it generates:

• Knowledge Guides — structured review notes with MathJax formulas, dual-color highlighting (key points in bold black, explanations in lighter gray), priority tags (must-know / key / frequent / info), and auto-generated table of contents
• Interactive Chapter Quizzes — click to answer, one-click grading, per-question correct/incorrect badges, detailed explanations, and common mistake warnings. Supports 9 question types including calc and code — automatically chosen by subject
• Knowledge Graph — interactive left-root/right-leaf tree layout with dependency dashed lines, hub-concept stars, hover tooltips, persistent inline note cards (text + paste images), search, and zoom
• Mock Final Exam — full exam paper with answer key, blueprint scoring exactly 100 points, chapter coverage, difficulty gradient, and interactive answer sheet

Open in any browser. Ctrl+P to print as PDF. MathJax renders formulas perfectly.

The universal pain of finals week: scattered lecture files, no clear sense of exam priorities, no reliable practice questions.

ExamPass reads your course materials with Claude, extracts key concepts with logical narratives, and generates self-grading quizzes. Students use it to study smarter. Instructors use it to create exercises and assignments in seconds.

PPTX · DOCX · PDF (with image recognition via multimodal analysis)

git clone https://github.com/WUBING2023/ExamPass-Assistant.git
cd ExamPass-Assistant
pip install -r requirements.txt

The default /exampass command orchestrates 5 specialized sub-agents:

All intermediate artifacts land in .epa_work/. The orchestrator (main Claude) only schedules — content is produced by sub-agents following agent cards in agents/.

from scripts.template_engine import save_knowledge_html, save_test, save_graph_html
from scripts.knowledge_graph import skeleton_to_graph_tree

# Knowledge guide — pass HTML body directly (engine adds H1 + TOC)
body = '<h2>1. Sequence Modeling Basics</h2>\n<h3>1.1 What is Sequence Data</h3>\n<p>...</p>'
save_knowledge_html(body, 'knowledge.html', 'Chapter 15')

# Interactive quiz — pass question data, get a self-grading page
questions = [
    {"type": "choice", "points": 2,
     "question": "What is the core function of a language model?",
     "options": ["Translation", "Estimating sentence probability",
                 "Tokenization", "Object recognition"],
     "answer": 1,
     "explanation": "A language model computes P(w1,...,wT)...",
     "pitfall": "Don't confuse language models with translation systems."},
]
save_test(questions, 'quiz.html', 'Chapter 15', '100 points', duration_minutes=30)

# Knowledge graph — convert skeleton to interactive DAG visualization
import json
with open('knowledge_skeleton.json') as f:
    skeleton = json.load(f)
tree = skeleton_to_graph_tree(skeleton)
save_graph_html(tree, 'graph.html', tree['title'])

EPA/
├── SKILL.md                    # /exampass entry point (command routing)
├── agents/                     # Sub-agent cards (methodology + prompt)
│   ├── skeleton-agent.md       # Knowledge architect — builds chapter→KC DAG
│   ├── notes-agent.md          # Note writer — deep-learning mode narratives
│   ├── item-agent.md           # Question writer — subject-aware question types
│   ├── reviewer-agent.md       # Content reviewer — correctness & completeness
│   └── solver-agent.md         # Exam solver — two-pass verification
├── scripts/                    # Core Python modules
│   ├── run_exampass.py         # Single-script extraction entry
│   ├── scanner.py              # Recursive scanning & grouping
│   ├── extractor.py            # Unified extraction dispatcher
│   ├── extract_pptx.py         # PPTX extraction (text + tables + images)
│   ├── extract_docx.py         # DOCX extraction
│   ├── extract_pdf.py          # PDF extraction
│   ├── image_extractor.py      # Image extraction for multimodal analysis
│   ├── ocr_backend.py          # OCR fallback for non-multimodal models
│   ├── template_engine.py      # HTML template engine (knowledge, test, graph)
│   ├── knowledge_graph.py      # Skeleton-to-graph-tree converter
│   ├── html_generator.py       # Fast generator
│   ├── generate_cached.py      # Cache-based instant re-runs
│   ├── knowledge_analyzer.py   # Knowledge list prompt builder
│   ├── test_generator.py       # Quiz generation prompt builder
│   ├── exam_generator.py       # Final exam prompt builder
│   ├── web_research.py         # Web research
│   └── utils.py                # Shared utilities
├── templates/                  # CSS, JS & HTML templates
│   ├── base.css                # Shared styles (warm paper, dual-color)
│   ├── test.css                # Interactive quiz styles
│   ├── graph.css               # Knowledge graph styles (tree layout)
│   ├── graph.js                # Graph renderer (D3 — dashed deps, tooltips, note cards)
│   ├── page_template.html      # HTML page shell
│   ├── graph_template.html     # Graph HTML shell
│   ├── test_js_template.js     # Quiz JS template
│   └── test_labels.json        # Chinese UI labels
├── tests/                      # 123 test cases
└── requirements.txt

• Development & Maintenance: @WUBING2023
• Inspirational Contribution: yaxing@cvc.uab.es
• Testing: @YeMoonlight
• Testing: @Yuzhihan-zyr

CC BY-NC 4.0 — free to use, modify, and share for non-commercial purposes. Commercial use requires a separate license.

Copyright (c) 2025 ExamPass Assistant Contributors