Description
#AI-Agent #Skill Generation #Knowledge Base #PDF Parsing #EPUB #DOCX #Markdown #ClaudeCode #CopilotCLI #Amp #Codex #Open Source Tools
Book-to-Skill is a tool specifically designed to convert books and documents into AI Agent Skills. In simple terms, you can directly input PDF, EPUB, DOCX, or Markdown files, and it will automatically analyze the content, extracting core models, chapter structures, key terms, methodologies, and reference materials, ultimately organizing them into a standard SKILL.md.
Compared to feeding the entire book into the AI context for every question, Book-to-Skill is more like processing a book in advance into an AI-accessible "skill package": when asked about any content, the corresponding chapter is loaded as needed. According to the project's published test data, this approach can reduce token consumption by approximately 24×–51×, which is particularly valuable for lengthy books and large databases.
It is not limited to e-books. Internal documents, research papers, technical specifications, product materials, and brand manuals can also be organized into Skills using this method. Originally, it was just about "having the AI read these materials"; after conversion, it is closer to enabling the AI to actually perform tasks based on the knowledge and methods in the materials.
Multi-format Document Import: Supports processing common book and document formats such as PDF, EPUB, DOCX, and Markdown, saving the hassle of manually splitting books and copying content.
Automatic Extraction of Core Knowledge: Able to organize chapters, core models, important terms, and key knowledge points from the original materials, converting lengthy content into a structure more suitable for AI Agents.
Methodology Organization: In addition to summarizing "what the book covers", it further extracts methods, processes, and actionable knowledge from the book, making the content more suitable as a practical working basis for Agents.
Automatic Creation of Reference Materials: Organizes important concepts and information into easily accessible reference content, reducing the need for Agents to sift through large amounts of irrelevant text to find a knowledge point.
Generate Standard SKILL.md: After processing, it directly outputs the Skill file, which can be used to build AI skills tailored to specific books, papers, or corporate materials.
Compatible with Various AI Programming Agents: The generated Skills can be used in tools like Claude Code, Copilot CLI, Amp, and can also be adapted for use in Codex and other Agent environments.
On-demand Chapter Loading: When in use, there is no need to repeatedly add the entire book to the context; instead, the corresponding knowledge and chapters are loaded based on the current question, making it more user-friendly for hundreds or even thousands of pages of materials.
Reduce Token Consumption: The project's published test results show that compared to the method of entering the entire material into the context, it can achieve approximately 24×–51× reduction in tokens, while also minimizing the occupation of context space by a large amount of irrelevant content.
Not Just E-books: Company knowledge bases, internal documents, papers, technical standards, operational specifications, brand manuals, and other materials can also be converted in a similar way, effectively processing static documents into knowledge and skills callable by Agents.
Book-to-Skill is a tool specifically designed to convert books and documents into AI Agent Skills. In simple terms, you can directly input PDF, EPUB, DOCX, or Markdown files, and it will automatically analyze the content, extracting core models, chapter structures, key terms, methodologies, and reference materials, ultimately organizing them into a standard SKILL.md.
Compared to feeding the entire book into the AI context for every question, Book-to-Skill is more like processing a book in advance into an AI-accessible "skill package": when asked about any content, the corresponding chapter is loaded as needed. According to the project's published test data, this approach can reduce token consumption by approximately 24×–51×, which is particularly valuable for lengthy books and large databases.
It is not limited to e-books. Internal documents, research papers, technical specifications, product materials, and brand manuals can also be organized into Skills using this method. Originally, it was just about "having the AI read these materials"; after conversion, it is closer to enabling the AI to actually perform tasks based on the knowledge and methods in the materials.
Software Features
Multi-format Document Import: Supports processing common book and document formats such as PDF, EPUB, DOCX, and Markdown, saving the hassle of manually splitting books and copying content.
Automatic Extraction of Core Knowledge: Able to organize chapters, core models, important terms, and key knowledge points from the original materials, converting lengthy content into a structure more suitable for AI Agents.
Methodology Organization: In addition to summarizing "what the book covers", it further extracts methods, processes, and actionable knowledge from the book, making the content more suitable as a practical working basis for Agents.
Automatic Creation of Reference Materials: Organizes important concepts and information into easily accessible reference content, reducing the need for Agents to sift through large amounts of irrelevant text to find a knowledge point.
Generate Standard SKILL.md: After processing, it directly outputs the Skill file, which can be used to build AI skills tailored to specific books, papers, or corporate materials.
Compatible with Various AI Programming Agents: The generated Skills can be used in tools like Claude Code, Copilot CLI, Amp, and can also be adapted for use in Codex and other Agent environments.
On-demand Chapter Loading: When in use, there is no need to repeatedly add the entire book to the context; instead, the corresponding knowledge and chapters are loaded based on the current question, making it more user-friendly for hundreds or even thousands of pages of materials.
Reduce Token Consumption: The project's published test results show that compared to the method of entering the entire material into the context, it can achieve approximately 24×–51× reduction in tokens, while also minimizing the occupation of context space by a large amount of irrelevant content.
Not Just E-books: Company knowledge bases, internal documents, papers, technical standards, operational specifications, brand manuals, and other materials can also be converted in a similar way, effectively processing static documents into knowledge and skills callable by Agents.
