book-to-skill: Transform Technical Books into AI Agent Skills

Introduction

book-to-skill is an innovative Python project by virgiliojr94 designed to revolutionize how you interact with technical documentation. It transforms any technical book, document folder, or collection of sources into a unified agent skill. This skill can then be loaded on demand by AI agents such as GitHub Copilot CLI, Amp, or Claude Code, making your knowledge base instantly accessible and actionable. With book-to-skill, you can convert PDFs, EPUBs, DOCXs, and other formats into a structured skill that your agent can query for precise, hallucination-free answers.

Installation

To get started with book-to-skill, you have a few straightforward options. The recommended method is via the cross-agent skills CLI:

# One command, any host, via the cross-agent skills CLI:
npx skills add virgiliojr94/book-to-skill

Alternatively, you can manually clone the repository into your agent's skills folder. This registers /book-to-skill as a command:

git clone https://github.com/virgiliojr94/book-to-skill.git ~/.claude/skills/book-to-skill
# For Copilot CLI: ~/.copilot/skills/
# For Amp/cross-agent: ~/.agents/skills/

The project is built in Python and may require additional extractors for specific file formats, for example, pdftotext for text-heavy PDFs or docling for technical books with code and tables. You can check your setup and identify missing dependencies with the command: python3 scripts/extract.py --check. For a comprehensive guide on installation and optional extractors, refer to the official documentation.

Examples

book-to-skill simplifies the process of turning complex documents into actionable knowledge for your AI agent. The workflow involves three simple steps:

1. Point: Direct the tool to your source file, folder, or glob, for example: /book-to-skill ./my-book.pdf.
2. Distill: The tool processes the input, distilling the book into a structured skill, including frameworks, decision rules, anti-patterns, and per-chapter files. It focuses on structure, not just a summary.
3. Load: Your agent loads the skill on demand. You can then ask questions like /my-book replication, and the agent will read the relevant chapter and answer from the actual content, ensuring accuracy and preventing hallucination.

When you run /book-to-skill your-book.pdf, it generates a comprehensive skill directory with files such as:

• SKILL.md: Contains core mental models and a chapter index.
• chapters/ch01-*.md: Individual files for each chapter, loaded only when needed.
• glossary.md: An alphabetically sorted list of key terms with chapter references.
• patterns.md: A collection of techniques, algorithms, and design patterns.
• cheatsheet.md: Decision tables and quick-reference rules.

Beyond traditional books, book-to-skill is highly versatile. It can process internal documentation, brand and design systems, research clusters, and technical specifications, transforming any frequently referenced structured prose into an easily queryable skill.

Why Use It

Many technical professionals face challenges in retaining information from extensive technical books or documents. Common workarounds, such as searching PDFs or taking manual notes, often prove inefficient or lead to forgotten content. Traditional AI agents, when fed raw book content, may hallucinate or struggle to provide precise answers due to context limitations.

book-to-skill directly addresses these issues by converting your documents into a structured skill that your agent can load on demand. This approach ensures that when you ask a question, the agent accesses the correct, pre-processed content, delivering accurate and reliable information. This integration makes the book an active part of your workflow, rather than a static reference.

A significant advantage of book-to-skill is its efficiency in token usage. By structuring the content once during conversion, it avoids the "Discovery Loop Tax" incurred by agents that repeatedly navigate and re-process raw documents. This results in 24x–51x fewer tokens required to answer a question compared to simply dumping the entire book into context, leading to faster responses and reduced operational costs for LLM interactions.

Links

• GitHub Repository: https://github.com/virgiliojr94/book-to-skill
• Documentation: https://github.com/virgiliojr94/book-to-skill/tree/master/docs
• Sponsor the Project: https://github.com/sponsors/virgiliojr94