This project addresses the challenge of effectively retaining and accessing knowledge from technical books, transforming them into interactive Claude Code skills. It targets users who need to deeply integrate book content into their workflow, offering a solution that provides direct, context-aware answers and avoids the limitations of simple PDF searching or generic LLM queries. The primary benefit is making complex technical information readily available and actionable during work.

How It Works

The system processes PDF or EPUB files through a Python script (extract.py) that first determines if the book is "technical" or "text-heavy." For technical PDFs, it uses docling to preserve markdown tables and code blocks, while text-heavy PDFs are processed by faster tools like pdftotext, PyPDF2, or pdfminer.six. EPUBs are handled by ebooklib or the standard zipfile library. Claude then analyzes the extracted content to generate structured outputs, including a core SKILL.md with mental models, individual chapter files loaded on-demand, a glossary, a patterns file, and a cheatsheet. This approach prioritizes density and practitioner-focused insights over raw text, enabling reasoning rather than simple retrieval.

Quick Start & Requirements

To install, copy SKILL.md and scripts/extract.py into ~/.claude/skills/book-to-skill/. Usage involves running /book-to-skill <path-to-pdf-or-epub> [skill-name-slug] within a Claude Code session. Prerequisites include installing at least one extraction tool: poppler-utils (for pdftotext), PyPDF2, pdfminer.six, or docling for PDFs, and ebooklib with beautifulsoup4 or the built-in zipfile for EPUBs.

Highlighted Details

• Generates a comprehensive skill structure including core mental models, chapter summaries, a glossary, patterns, and a cheatsheet.
• Employs an on-demand chapter loading mechanism to manage token budgets effectively.
• Differentiates itself from Retrieval-Augmented Generation (RAG) by focusing on extracting and structuring authorial frameworks for deeper reasoning.
• Enables grounding LLM responses in the user's specific book copy, mitigating training data drift and hallucination, and supporting niche or undocumented books.

Maintenance & Community

The provided README does not contain specific details regarding maintainers, community channels (like Discord or Slack), sponsorships, or a public roadmap.

Licensing & Compatibility

The project is released under the MIT license, which is permissive and generally compatible with commercial use and integration into closed-source projects.

Limitations & Caveats

Setup requires installing specific external Python packages or system utilities for PDF/EPUB extraction. The tool is designed for deep integration with a single book at a time, contrasting with multi-book search solutions. Functionality is dependent on the Claude Code environment.