Lecture Notes Creator — AI-Powered PDF-to-Notes for Students

中文文档 | English

Turn confusing course PDFs into clear, page-by-page self-study notes — with AI extraction, structured writing, and iterative quality review.

How It Works

flowchart LR
    A["📄 Course PDF"] --> B["🔍 MinerU<br/>Extraction"]
    B --> C["🖼️ Image<br/>Understanding"]
    C --> D["📋 Structure<br/>Analysis"]
    D --> E["✍️ Note<br/>Writing"]
    E --> F{"🔍 AI Review<br/>7-Dim Score"}
    F -->|"Score ≥ 8"| G["✅ Final Notes"]
    F -->|"Needs Work"| E
    G --> H["☁️ Notion<br/>Sync"]

Loading

Why Lecture Notes Creator?

Staring at a 200-page course PDF the night before the exam? We've been there.

Most students either:

• Read passively — highlight everything, remember nothing
• Copy-paste slides — end up with the same confusing content in a different format

Lecture Notes Creator takes a different approach:

Traditional	Lecture Notes Creator
Dense slides with abbreviations	Step-by-step explanations with full context
Skipped derivations ("obviously...")	Complete reasoning chains from intuition to math
No way to check coverage	Page-by-page mapping — every slide accounted for
One-pass reading	Multi-round AI review until quality threshold is met

Key Features

🔬 High-Fidelity Extraction

MinerU VLM mode preserves formulas (LaTeX), tables (HTML), and figures from any PDF. No more broken equations or garbled text.

📖 Page-Driven Structure

Notes strictly follow P1→P2→P3 order. Every page mapped, nothing missed — cross-reference back to the original slides in seconds.

🧠 Adaptive Writing Styles

Traditional courses (physics/chem) follow analogy → derivation → meaning. Slide-based (CS/AI) follows motivation → mechanism → application. The LLM auto-detects which style fits your PDF.

🔄 AI Review Loop

A sub-agent role-playing an undergraduate student scores each chapter across 7 dimensions. The loop iterates until score ≥8/10 — typically 1-2 rounds.

☁️ One-Click Notion Sync

Push notes to your Notion workspace with proper formatting (headings, LaTeX, tables, callouts). No manual formatting needed.

Quick Start

📖 Full installation guide: INSTALLATION.md (English) | Quick start: QUICKSTART.md (English)

Prerequisites

• Python 3.10+
• MinerU API Key (from mineru.net)
• (Optional) Notion API Key

Install

git clone https://github.com/ZelinZhou-THU/lecture-notes-creator.git
cd lecture-notes-creator
pip install -r requirements.txt

Configure your .env file:

MINERU_TOKEN=your_mineru_api_key_here
NOTION_API_KEY=your_notion_api_key_here  # Optional

Then open the project in OpenCode and give it your PDF. That's it.

📖 Detailed usage steps

1. Analyze PDF (optional, only needed if >200 pages)

   python scripts/split_pdf.py analyze "path/to/courseware.pdf" --preview 3

2. Extract content (generates .bat file, double-click to run)

   python scripts/create_extraction_bat.py "path/to/courseware.pdf" --output ./output_dir/mineru --mode auto

3. Tell OpenCode "extraction done" — it checks status and continues

4. Sub-agent analyzes images (optional, recommended for image-heavy PDFs)

5. OpenCode handles the rest:

   • Identifies courseware type (traditional / slide-based)
   • Writes lecture notes page by page
   • Each chapter goes through AI review iteration
   • Optionally uploads to Notion

Project Structure

lecture-notes-creator/
├── SKILL.md                          # Skill definition (core workflow)
├── .opencode/agents/
│   ├── lecture-reviewer.md           # Sub-agent: quality review
│   └── image-describer.md            # Sub-agent: image understanding
├── scripts/
│   ├── split_pdf.py                  # PDF analyze / pre-split / merge
│   ├── extract_pdf.py                # Page screenshots (fallback)
│   ├── mineru_extract.py             # MinerU API calls
│   ├── create_extraction_bat.py      # Generate .bat/.sh for extraction
│   ├── run_mineru_standalone.py      # Run MinerU outside OpenCode (cross-platform)
│   ├── wait_for_extraction.py        # Wait for extraction completion
│   ├── reconstruct_full_md.py        # Rebuild Markdown from JSON
│   ├── backfill_image_descriptions.py
│   └── save_batch_json.py
├── deps/
│   ├── mineru/                       # MinerU skill reference
│   └── notion/                       # Notion skill (built-in)
│       └── scripts/                  # Notion upload scripts
├── references/
│   ├── writing-style-guide.md        # Writing style reference
│   ├── review-prompt.md              # Review prompt template
│   ├── notion-upload.md              # Notion upload guide
│   ├── mineru-api-guide.md           # MinerU API reference
│   └── output-structure.md           # Output file structure
├── docs/
│   ├── INSTALLATION.md               # Installation guide (zh)
│   ├── INSTALLATION_EN.md            # Installation guide (en)
│   ├── QUICKSTART.md                 # Quick start guide (zh)
│   └── QUICKSTART_EN.md             # Quick start guide (en)
└── requirements.txt

Credits

Built with:

• MinerU — High-fidelity PDF extraction (Modified Apache License 2.0)
• OpenClaw Notion Skill — Notion API integration (MIT License)
• LobeHub MinerU Skill — Skill reference (MIT License)

License

Apache License 2.0