phd-deepread-workflow 0.1.1

Transform academic PDFs into structured literature notes and critical-thinking canvases for Obsidian

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PhD Deep Read Workflow

Transform academic PDFs into structured literature notes and critical-thinking canvases for Obsidian using AI-assisted analysis.

🎯 What is PhD Deep Read?

PhD Deep Read is a sophisticated workflow that helps researchers and PhD students process academic literature efficiently. It transforms raw PDFs into:

1. Structured literature notes following a comprehensive academic template
2. Critical-thinking canvases with 9 interconnected nodes for deep analysis
3. Extracted text and images using a smart Text-First decision tree
4. Claude Code-assisted analysis for high-quality note generation

Perfect for literature reviews, dissertation research, and systematic knowledge building in Obsidian.

✨ Key Features

🚀 Smart PDF Extraction

• Text-First Decision Tree: Pre-scans PDFs, uses fast PyMuPDF extraction for searchable text (80%+ of academic PDFs)
• Intelligent OCR Fallback: Only uses Tesseract OCR for scanned/complex pages
• Image Extraction: Preserves figures, tables, and diagrams as embedded images
• Metadata Tracking: Records extraction method per page for transparency

📝 AI-Assisted Note Generation

• Comprehensive Template: 175+ line .clauderules template with YAML frontmatter, Dataview callouts, and academic sections
• Claude Code Integration: Uses Claude's reasoning for critical analysis and synthesis
• Wikilink Rich: Extensive linking of concepts, methods, proteins, and diseases
• Obsidian Ready: Fully compatible with Dataview plugin and Obsidian Canvas

🧠 Critical-Thinking Canvases

• 9 Interconnected Nodes: Core argument, assumptions, evidence assessment, alternative explanations, methodological critique, personal relevance, future directions, critical questions, hypothesis center
• Visual Analysis: Spatial arrangement facilitates deep critical thinking
• JSON Format: Compatible with Obsidian Canvas plugin

⚙️ Automated Workflow

• Batch Processing: Process entire directories of PDFs overnight
• Quality Verification: Automated checks for format consistency and quality
• Modular Commands: Separate commands for each workflow stage
• Configurable: Adjust thresholds, templates, and output formats

🏗️ Architecture

graph TD
    A[PDF Input] --> B{Text-First Decision Tree}
    B --> C[Searchable?]
    C -->|Yes| D[PyMuPDF<br/>Fast Text Extraction]
    C -->|No| E[Tesseract OCR<br/>Scanned Pages]

    D --> F[Markdown + Images + Metadata]
    E --> F

    F --> G[Claude Code Analysis]
    G --> H[Structured Literature Note]
    F --> I[Canvas Template]
    I --> J[Critical-Thinking Canvas]

    H --> K[Obsidian Integration]
    J --> K

    K --> L[Verified Output]

📦 Installation

Prerequisites

• Python 3.10+ and pip
• Tesseract OCR (optional, for scanned PDFs)
• Claude Code (for note generation)

Quick Install

# Clone the repository
git clone https://github.com/Helen-insights/phd-deepread-workflow.git
cd phd-deepread-workflow

# Install Python dependencies
pip install -r requirements.txt

# Install Tesseract OCR (optional but recommended)
# macOS:
brew install tesseract
# Ubuntu/Debian:
sudo apt install tesseract-ocr

# Install Python OCR wrapper
pip install pytesseract pillow

# Verify installation
python scripts/verify.py --extract test_paper.pdf

As a Claude Code Skill

# Copy the skill to your Claude Code skills directory
cp -r phd-deepread-workflow ~/.claude/skills/phd-deepread

# Use the skill in Claude Code
phd-deepread setup
phd-deepread extract paper.pdf

Docker Installation

For consistent environments or containerized deployment:

# Build the Docker image
docker build -t phd-deepread .

# Run a single extraction
docker run -v $(pwd)/input:/input -v $(pwd)/output:/output \
  phd-deepread extract /input/paper.pdf --output /output/

# Or use Docker Compose
docker-compose up --build

See Dockerfile and docker-compose.yml for advanced configurations.

🚀 Quick Start

Process a Single Paper

# 1. Extract text and images
phd-deepread extract paper.pdf --output markdown_output/

# 2. Generate structured literature note (requires Claude Code)
phd-deepread generate markdown_output/paper/ --template templates/.clauderules

# 3. Create critical-thinking canvas
phd-deepread canvas markdown_output/paper/ --output structured_notes/

Batch Process a Directory

# Process all PDFs in a directory
phd-deepread batch papers/ --output literature-notes/

# Limit to first N pages (for testing)
phd-deepread batch papers/ --output literature-notes/ --max-pages 3

Interactive Guide

# Show workflow guide with decision-tree visualization
phd-deepread guide

📖 Detailed Usage

Stage 1: Text-First PDF Extraction

The extraction uses a smart decision tree:

phd-deepread extract paper.pdf \
  --output markdown_output/ \
  --threshold 100 \        # Min chars for "searchable"
  --percentage 0.8 \       # Use PyMuPDF if 80%+ pages searchable
  --lang eng \            # OCR language
  --disable-image-extraction  # Skip images if needed

Output Structure:

markdown_output/paper/
├── paper.md              # Raw markdown with embedded images
├── paper_meta.json       # Metadata, extraction methods per page
├── blocks.json          # Block-level segmentation data
└── _page_*_*.png        # Extracted images

Stage 2: Structured Note Generation

Uses Claude Code with the .clauderules template:

phd-deepread generate markdown_output/paper/ \
  --template templates/.clauderules \
  --output structured_notes/ \
  --skeleton              # Generate placeholder note without Claude

Template Features:

• YAML frontmatter (category, tags, citekey, status, dateread)
• Dataview callouts ([!Citation], [!Synthesis], [!Metadata], [!Abstract])
• 7 academic sections with critical analysis
• Wikilinks for concepts, methods, tools

Stage 3: Critical-Thinking Canvas

Creates 9-node JSON Canvas for deep analysis:

phd-deepread canvas markdown_output/paper/ \
  --output structured_notes/ \
  --template templates/critical-thinking.canvas

Canvas Nodes:

1. core-argument - Primary claim and logical chain
2. assumptions - Explicit, implicit, questionable assumptions
3. evidence-assessment - Strength of evidence
4. alternative-explanations - Competing hypotheses
5. methodological-critique - Study limitations
6. personal-relevance - Connections to your research
7. future-directions - Research goals
8. critical-questions-enhanced - Hypothesis testing questions
9. hypothesis-center - Hypothesis re-evaluation

Stage 4: Verification

Quality checks and pattern matching:

phd-deepread verify --all literature-notes/

🔧 Configuration

Custom Templates

Modify templates/.clauderules for different academic fields:

category: literaturenote
tags:
  - #{{Field}}           # e.g., #Neuroscience, #Bioinformatics
  - #{{Topic1}}
  - #{{Topic2}}
citekey: {{camelCase: FirstAuthor+FirstWordOfTitle+Year}}
# ... rest of template

Extraction Parameters

Adjust in scripts/extract.py or via command line:

Parameter	Default	Description
--threshold	100	Min characters to consider page searchable
--percentage	0.8	Use PyMuPDF if this % of pages are searchable
--lang	eng	Tesseract OCR language code
--force-ocr	false	Force OCR for all pages
--force-text	false	Force PyMuPDF for all pages
--no-ocr	false	Disable OCR entirely

Output Directories

Configure in config/config.yaml:

# Directory paths
directories:
  markdown_output: "./markdown_output"
  structured_notes: "./structured_literature_notes"
  canvas_templates: "./canvas_templates"
  generation_prompts: "./generation_prompts"

# Extraction thresholds
extraction:
  searchable_threshold: 100
  searchable_percentage: 0.8
  ocr_language: "eng"

See config/config.yaml for all available options.

📊 Performance

Scenario	Time per Paper	Accuracy	Best For
All searchable text	10-30 minutes	~100%	Modern digital PDFs
Mixed (80% text, 20% OCR)	30-60 minutes	~98%	Typical academic PDFs
All OCR required	60-120 minutes	~95%	Scanned documents

Typical workflow: 27-80 minutes per paper total (extraction + note generation + canvas creation)

🛠️ Troubleshooting

Common Issues

Tesseract OCR not installed:

# macOS
brew install tesseract

# Ubuntu/Debian
sudo apt install tesseract-ocr

# Python wrapper
pip install pytesseract pillow

PyMuPDF missing:

pip install PyMuPDF

Missing images in extraction:

• Check PDF contains extractable images
• Ensure --disable-image-extraction not set
• Verify PyMuPDF version supports image extraction

Virtual environment issues:

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/macOS
# or .venv\Scripts\activate  # Windows
pip install -r requirements.txt

Claude Code integration:

• Ensure Claude Code is installed and authenticated
• Check skill is properly installed in ~/.claude/skills/
• Verify skill has necessary permissions

Debug Mode

Run with verbose output:

phd-deepread extract paper.pdf --verbose --debug

Check logs in logs/ directory (if configured).

🔄 Integration with Other Tools

Zotero

• Use Zotero citation keys as citekey in frontmatter
• Export PDFs from Zotero to processing directory
• Import generated notes back into Zotero as linked files

Obsidian

• Notes ready for Dataview queries
• Canvases work with Obsidian Canvas plugin
• Wikilinks connect to existing or future notes
• Use with Obsidian Git for version control

Reference Managers

• BibTeX export for generated citations
• RIS format integration (planned)
• DOI lookup and metadata fetching (planned)

📚 Examples

See the examples/ directory for:

• example-output.md - Complete structured literature note
• example-canvas.canvas - 9-node critical-thinking canvas
• test_paper.pdf - Sample PDF for testing

Example output from the batch test is in batch_test_output/.

🧪 Testing

Run the test suite:

# Run all tests
python -m pytest tests/ -v

# Test specific component
python scripts/verify.py --extract test_paper.pdf
python scripts/verify.py --note examples/example-output.md
python scripts/verify.py --canvas examples/example-canvas.canvas

Note: For the extraction test, use your own PDF file named test_paper.pdf in the examples directory, or replace the command with a path to your test PDF.

Continuous Integration: Tests run automatically on GitHub Actions for each commit and pull request. See .github/workflows/test.yml.

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Clone and install development dependencies
git clone https://github.com/Helen-insights/phd-deepread-workflow.git
cd phd-deepread-workflow
pip install -r requirements-dev.txt
pre-commit install  # Install git hooks

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Claude Code for AI-assisted note generation
• PyMuPDF for fast PDF text extraction
• Tesseract OCR for optical character recognition
• Obsidian for the excellent note-taking platform
• All contributors who help improve this workflow

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

📈 Roadmap

• Web UI for configuration and monitoring
• Integration with more reference managers (Mendeley, EndNote)
• Advanced layout detection for complex PDFs
• Multi-language support for OCR and analysis
• Plugin system for custom templates and processors
• Cloud processing for large PDF collections
• Mobile app for on-the-go paper processing

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Made with ❤️ for the academic community

If this workflow helps your research, consider giving it a ⭐ on GitHub!