book-to-skill: Turn a Technical Book Into a Claude Code Skill, Not a Summary

Book-to-skill, an MIT-licensed tool with 15,259 GitHub stars, converts technical books, document folders, or paper stacks into structured Agent Skills (SKILL.md plus per-chapter files) for Claude Code, GitHub Copilot CLI, or Amp, loading only the needed chapter on demand. The tool, updated August 3, 2026, generates files including SKILL.md (~4,000 tokens), per-chapter files (~1,000 tokens each), glossary.md (~1,500 tokens), patterns.md (~2,000 tokens), and cheatsheet.md (~1,000 tokens), supporting formats such as PDF, EPUB, DOCX, and Markdown.

book-to-skill: Turn a Technical Book Into a Claude Code Skill, Not a Summary book-to-skill is an MIT-licensed tool that distills a technical book, doc folder, or paper stack into a structured Agent Skill SKILL.md plus per-chapter files for Claude Code, GitHub Copilot CLI, or Amp — loading only the chapter you need instead of dumping the whole book into context. - ⭐ 15259 - Python - MIT - Updated 2026-08-03 Claude Code Skill Authoring Guide https://dibi8.com/resources/llm-frameworks/claude-code-skill-authoring-guide-2026/ • k-skill: 100+ Skills That Turn Any AI Agent Into a Korea Local https://dibi8.com/resources/ai-tools/k-skill-korea-agent-skills-2026/ Project logo — from github.com/virgiliojr94/book-to-skill What Is book-to-skill? what-is-book-to-skill book-to-skill turns a technical book, document folder, or paper stack into a unified Agent Skill — ready to study, reference, and use while you work in GitHub Copilot CLI, Amp, or Claude Code . The core idea, in the project’s own words: “You buy a great technical book. You read it once. Three months later you can’t remember chapter 7 existed.” Instead of hoping the agent has hallucination-free knowledge of the book, or re-searching a PDF every time, book-to-skill pre-extracts the book’s structure once and lets the agent load exactly the chapter it needs, on demand. 🔗 GitHub : https://github.com/virgiliojr94/book-to-skill https://github.com/virgiliojr94/book-to-skill MIT licensed, at 15,200+ GitHub stars , with a commit from July 31, 2026 , built on the open Agent Skills https://github.com/agentskills/agentskills standard that Copilot CLI, Amp, and Claude Code all share SKILL.md format — so a skill generated once works across any of those three hosts. How It Works: 3 Steps how-it-works-3-steps Point it at a file, folder, or glob — /book-to-skill ./my-book.pdf It distills the book into a skill — mental models, decision rules, anti-patterns, and per-chapter files. Structure, not a summary. Your agent loads it on demand — ask /my-book replication and it reads the right chapter and answers from the real content. What Gets Generated what-gets-generated Running /book-to-skill your-book.pdf creates a full skill in your agent’s skills directory ~/.claude/skills/<slug / for Claude Code, ~/.copilot/skills/<slug / for Copilot CLI, ~/.agents/skills/<slug / for Amp or cross-agent use : | File | Purpose | Size | |---|---|---| SKILL.md | Core mental models + chapter index | ~4,000 tokens | chapters/ch01- .md … | One file per chapter, loaded on-demand | ~1,000 tokens each | glossary.md | Every key term, alphabetically sorted with chapter refs | ~1,500 tokens | patterns.md | All techniques, algorithms, and design patterns | ~2,000 tokens | cheatsheet.md | Decision tables and quick-reference rules | ~1,000 tokens | Chapter files don’t count against the skill’s token budget until you actually ask about that topic. Usage usage /book-to-skill <path-to-document-folder-or-glob ... skill-name-slug Process several files together into a unified skill /book-to-skill ~/papers/paper1.pdf ~/notes/export.txt unified-research Process a whole folder /book-to-skill ~/workspace/project-docs/ project-knowledge Process files matching a glob /book-to-skill "~/books/ .epub" my-library Fold new material into an existing skill /book-to-skill ~/articles/new-paper.pdf ~/.claude/skills/project-knowledge Once generated, you use it like any skill: /designing-data-intensive-apps load core mental models /designing-data-intensive-apps replication find and explain a topic /designing-data-intensive-apps ch05 dive into chapter 5 Supported formats: PDF, EPUB, DOCX, TXT, Markdown, reStructuredText, AsciiDoc, HTML, RTF, MOBI/AZW/AZW3. For PDFs, python3 scripts/extract.py --check reports which extractor pdftotext, pypdf, pdfminer.six, or docling is installed for every format and what to install if something’s missing — and the tool auto-picks between “text-heavy” and “technical” code/tables/formulas extraction depending on the book. Beyond Books beyond-books The name says “book,” but the extraction works on any structured prose you re-read often: Internal documentation — ADRs, runbooks, onboarding guides folded into one queryable skill Brand & design systems — voice guidelines and component principles instead of skimming a 60-page PDF Research clusters — a stack of papers plus your own notes, merged and updatable as new material lands Specs & standards — RFCs, API contracts, compliance docs you reference but never memorize The project’s own heuristic: if you re-open a document often enough to wish you’d memorized it, it’s a candidate. The Discovery Loop Tax: Measured, Not Asserted the-discovery-loop-tax-measured-not-asserted A PDF-reading agent doesn’t just read — it navigates: fetching a table of contents, pulling more pages when it hits an unfamiliar term, backtracking. Every hop lands in conversation history and gets re-processed on every subsequent turn , forcing a sub-agent to compress what it read into a summary the main agent can’t fact-check against the source. book-to-skill pays that navigation cost once, at generation time. The README publishes a reproducible measurement tools/discovery tax.py across three real books: | Book size | Context-dump | Discovery loop | book-to-skill | vs. dump / loop | |---|---|---|---|---| | Think Python 2 119K tokens | 119,264 | 12,152 | ~5,000 | 24× / 2.4× | | Working Backwards 175K tokens | 175,253 | 33,444 | ~5,000 | 35× / 6.7× | | AI Engineering 256K tokens | 256,287 | 77,866 | ~5,000 | 51× / 15.6× | Honest caveats the project states itself : the discovery-loop figures are a one-time cost and a model using the book’s real chapter structure — a well-tuned agent might land nearer the best case. The context-dump cost, by contrast, recurs on every turn. The extraction also needs recognizable chapter headings to segment cleanly it detects Arabic, Roman, CJK, Korean, Thai, and several European heading forms — a titles-only book may not segment well. For a single one-off read, the project itself says a plain PDF agent is fine; book-to-skill wins when you return to the material repeatedly. “Isn’t This Just RAG?” — The Project’s Own Answer isnt-this-just-rag--the-projects-own-answer The FAQ addresses this head-on: raw text injection whether pasted or retrieved via RAG chunks is retrieval — the model searches for keyword/semantic matches in unstructured text. A book-to-skill chapter file is reasoning material — pre-extracted named frameworks, principles, and decision rules structured for application, not for search. The distinction matters most for technical books where the value is in the mental model , not in locating a specific sentence. book-to-skill vs. Alternatives book-to-skill-vs-alternatives | Approach | Token cost per question | Fact-checkable against source | Setup | |---|---|---|---| Paste whole book into context | Full book, every turn | Yes, but expensive | None | Agent searches the PDF live discovery loop | 2.4×-15.6× more than book-to-skill | Yes | None | book-to-skill | ~5,000 tokens core + 1 chapter | Yes — full source stays on disk | One-time generation | Generic RAG over chunked PDF | Varies, chunk-dependent | Partial — depends on chunk boundaries | Vector DB + embedding pipeline | Use Cases use-cases 1. Technical Reference You Actually Query 1-technical-reference-you-actually-query Load /designing-data-intensive-apps replication mid-project instead of trying to remember which chapter covered it, or re-reading the whole book. 2. Turning a docs/ Folder Into a Queryable Skill 2-turning-a-docs-folder-into-a-queryable-skill Point book-to-skill at an internal documentation folder once, then ask the resulting skill questions while coding instead of grepping through Markdown files by hand. 3. Merging a Research Paper Stack 3-merging-a-research-paper-stack Combine several related papers plus your own notes into one unified skill, updating it as new papers land via the fold-in usage pattern. 4. Onboarding Material That Doesn’t Go Stale in a Chat Log 4-onboarding-material-that-doesnt-go-stale-in-a-chat-log A brand guideline or architecture-decision skill that any team member’s agent can query, instead of a Slack thread nobody can find six months later. Related Repositories related-repositories | Repository | Purpose | |---|---| | SKILL.md format book-to-skill outputs, shared across Copilot CLI, Amp, and Claude Code Related Articles related-articles Claude Code Skill Authoring Guide https://dibi8.com/resources/llm-frameworks/claude-code-skill-authoring-guide-2026/ — for writing Agent Skills by hand, versus generating one automatically from a document k-skill: 100+ Skills That Turn Any AI Agent Into a Korea Local https://dibi8.com/resources/ai-tools/k-skill-korea-agent-skills-2026/ — another large-scale Agent Skills project, service-integration skills rather than document-distillation skills Conclusion conclusion book-to-skill makes a specific, well-argued bet: for knowledge you return to repeatedly, pre-extracting structure once beats both dumping raw text into context and letting an agent rediscover the same table of contents every session. The measured not just claimed 24×-51× token reduction, the honest caveats about when it doesn’t help, and the direct FAQ answers to the obvious “why not just use a big context window” objection all suggest a maintainer who’s thought carefully about the failure modes rather than just shipping a demo. Support across Claude Code, GitHub Copilot CLI, and Amp via the shared Agent Skills standard means the generated skill isn’t locked to one vendor either. Best for : Anyone who owns technical books, internal docs, or paper stacks they reference constantly but can never quite remember the details of — not a one-off PDF question, which a plain agent already handles fine. GitHub : https://github.com/virgiliojr94/book-to-skill https://github.com/virgiliojr94/book-to-skill Last updated: 2026-08-03