PhD Deep Read Workflow

What it does

PhD Deep Read packages a four-stage pipeline for turning academic PDFs into richly structured literature notes and critical-thinking canvases. It uses a Text-First decision tree (PyMuPDF for searchable pages with Tesseract OCR fallback) to extract text and images, then generates Obsidian-friendly markdown with YAML frontmatter and Dataview callouts. The skill also produces JSON Canvas files for deep analysis and includes verification steps to ensure output consistency.

When to use it

Use this skill when processing individual or batches of academic PDFs for literature reviews, generating reproducible notes for Obsidian, or when you need structured synthesis and critique (assumptions, evidence assessment, future directions). It's appropriate for researchers, graduate students, and knowledge workers preparing reading corpora.

What's included

• Scripts: workflow automation scripts (setup, extract, generate, canvas, run, verify, batch) — note: fetch output shows has_scripts=false but the SKILL describes CLI commands and templates
• References: templates and demo documentation are referenced in the repo
• Instructions: detailed stage-by-stage guidance (Text-First extraction, structured note generation, canvas creation, verification). Includes usage examples and troubleshooting tips.

Compatible agents

Works with agents that can run or orchestrate CLI/python tools (Claude Code, assistant shells, or local CLI wrappers). Best when the environment provides PyMuPDF and Tesseract for OCR and when the agent can read/write files for Obsidian integration.