学习研究来源：GitHub 协议：MIT 协议已标注

book-to-skill

把一本书的核心方法抽取成可被 Agent 调用的结构化技能。

28.7K GitHub stars 2026-09-05 星标同步 2026-07-16 收录时间

#books#skill#knowledge

快速判断

01这个 Skill 是干嘛的: 从一本书中抽取可操作的方法、规则和流程，封装成 Agent skill。
02它能解决什么问题: 解决读书笔记停留在摘要、书中方法无法在真实任务中被重复调用的问题。
03它适合谁来用: 适合希望把专业书籍转成长期工作方法的学习者、顾问和团队。

book-to-skill

把一本书的核心方法抽取成可被 Agent 调用的结构化技能。

主要能力

Converts books and documents (PDF, EPUB, DOCX, HTML, Markdown, plain text, RTF, MOBI/AZW with Calibre) into structured agent skills, extracting frameworks, mental models, principles, techniques, and anti-patterns. Use when the user wants to study a document through GitHub Copilot CLI, Amp, or Claude Code, apply an author's frameworks while working, or build a reusable knowledge base from a file.

使用方式

仓库级安装会保留完整目录；本次核验的入口是 SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

必须尊重原作版权，不能用生成结果替代或重制受保护的完整内容。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

<h1 align="center">book-to-skill</h1>

Turn any technical book, document folder, or collection of sources into a unified agent skill — ready to study, reference, and use while you work in GitHub Copilot CLI, Amp, or Claude Code.

🏆 #10 Python Repository of the Day and #25 Repository of the Day on Trendshift (May 23, 2026)

<a href="#-why">Why</a> · <a href="#-what-it-generates">What it generates</a> · <a href="#-beyond-books">Beyond books</a> · <a href="#-usage">Usage</a> · <a href="#-requirements">Requirements</a> · <a href="#-how-it-works">How it works</a> · <a href="#-the-discovery-loop-tax">Discovery Loop Tax</a> · <a href="#-faq">FAQ</a> · <a href="#-install">Install</a> · <a href="CHANGELOG.md">Changelog</a> · <a href="docs/PERFORMANCE.md">Performance</a> · <a href="docs/ARCHITECTURE.md">Architecture</a>

24×–51× fewer tokens than dumping the book into context to answer one question, measured on real books (<a href="#-the-discovery-loop-tax">how it's measured</a>).

How it works, in 3 steps:

Point it at a file, folder, or glob — /book-to-skill ./my-book.pdf
It distills the book into a skill — frameworks, 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, no hallucination.

---

🤔 Why

You buy a great technical book. You read it once. Three months later you can't remember chapter 7 existed.

The usual workarounds don't help:

📄 "Let me just search the PDF" → you get a list of pages, not answers
🧠 "I'll ask the agent about this book" → it either hallucinates or says it doesn't have the content
📝 "I'll take notes as I read" → you end up with a 200-line doc you never open again

book-to-skill solves this by turning the book into a structured skill your agent loads on demand.

Once installed, you just type /your-book-slug replication and the agent reads the right chapter and answers from the actual content. No hallucination. No digging through PDFs. The book becomes part of your workflow.

Works with any host that supports the open Agent Skills standard — GitHub Copilot CLI, Amp, and Claude Code all read the same SKILL.md format.

---

📦 What it generates

Running /book-to-skill your-book.pdf (or a folder, glob, or list of files) creates a full skill in your agent's skills directory (~/.copilot/skills/<slug>/ for Copilot CLI, ~/.agents/skills/<slug>/ for Amp or cross-agent, ~/.claude/skills/<slug>/ for Claude Code):

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 are loaded on-demand — they don't count against the skill budget until you ask about that topic.

---

🏢 Beyond books

The name says "book", but the input is any structured prose. The same extraction works on knowledge you own and re-read constantly:

Internal documentation — architecture decision records, runbooks, onboarding guides. Fold a whole docs/ folder into one skill and ask it while you code.
Brand & design systems — voice guidelines, tone-of-voice docs, component principles. Turn a brand book into a skill your team queries instead of skimming a 60-page PDF.
Research clusters — a stack of papers plus your own notes, merged into a single unified skill and updated as new material lands (see Update / fold-in).
Specs & standards — RFCs, API contracts, compliance docs you reference but never memorize.

If you re-open a document often enough to wish you'd memorized it, it's a candidate.

---

🚀 Usage

/book-to-skill <path-to-document-folder-or-glob>... [skill-name-slug]

Supported document formats: PDF, EPUB, DOCX, TXT, Markdown, reStructuredText, AsciiDoc, HTML, RTF, MOBI/AZW/AZW3.

Examples:

# Process several files together into a unified skill
/book-to-skill ~/papers/paper1.pdf ~/notes/export.txt unified-research

# Process all supported files in a folder together
/book-to-skill ~/workspace/project-docs/ project-knowledge

# Process files matching a glob pattern
/book-to-skill "~/books/*.epub" my-library

# Update/fold new material into an existing skill folder
/book-to-skill ~/articles/new-paper.pdf ~/.claude/skills/project-knowledge

After the skill is created, use it like any other agent 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
/designing-data-intensive-apps "what chapters do you have?"

In GitHub Copilot CLI you may need to run /skills reload after the file is written so the new skill appears in /skills list. Claude Code and Amp pick it up on the next session.

---

🔧 Requirements

The extractor tries tools in order per format and uses the first available. If nothing is installed, it tells you which command to run. Plain text, Markdown, reStructuredText and AsciiDoc need no extra deps.

Check your setup in one command: python3 scripts/extract.py --check prints which extractors are installed for every format and the exact command to install anything missing — no file needed.

PDF — choose by book type:

Book type	Tool	Install	Speed
Text-heavy (prose, few tables)	`pdftotext` (poppler)	`sudo apt install poppler-utils`	⚡ instant
Text-heavy fallback	`pypdf`	`pip3 install pypdf`	⚡ instant
Text-heavy fallback	`pdfminer.six`	`pip3 install pdfminer.six`	⚡ instant
Technical (code, tables, formulas)	`docling`	`pip3 install docling`	~1.5s/page

Before extraction begins, the skill asks you whether the book is technical or text-heavy and picks the right tool automatically. Docling preserves markdown tables and code blocks; pdftotext is faster for prose-only books.

EPUB:

Tool	Install	Quality
`ebooklib` + `beautifulsoup4`	`pip3 install ebooklib beautifulsoup4`	⭐⭐⭐ Best
stdlib `zipfile`	built-in — no install needed	⭐⭐ Always available

Other formats:

Format	Tool	Install
DOCX	`python-docx` (fallback: stdlib ZIP/XML)	`pip3 install python-docx`
HTML	`beautifulsoup4` (fallback: stdlib `html.parser`)	`pip3 install beautifulsoup4`
RTF	`striprtf` (fallback: regex)	`pip3 install striprtf`
MOBI / AZW / AZW3	Calibre `ebook-convert` (external app, not pip)	https://calibre-ebook.com/download
TXT / Markdown / reStructuredText / AsciiDoc	built-in	—

---

⚙️ How it works

One file · a folder · a glob · a list of paths
     │
     ▼
Step 1.5 — "Technical or text-heavy book?"
     │
     ├── technical → Docling  (tables + code blocks as markdown, ~1.5s/page)
     └── text      → pdftotext → pypdf → pdfminer  (instant)
     │
     ▼
scripts/extract.py <paths…> --mode <technical|text>
  per source: PDF → pdftotext/Docling · EPUB → ebooklib → stdlib zipfile · DOCX/HTML/RTF/…
  (one bad source is skipped with a warning; the rest still process)
     │
     ├── /tmp/book_skill_work/full_text.txt   (all sources merged, with source markers)
     └── /tmp/book_skill_work/metadata.json   (aggregated stats + per-source array)
               │
               ▼
          Claude analyzes structure
          (title, author, chapters, ToC — spanning all sources)
          ── or, if targeting an existing skill: folds new content in (Mode 4)
               │
               ▼
          Generates per-chapter summaries  (800–1,200 tokens each)
          technical → includes Code Examples + Reference Tables sections
          Generates glossary, patterns, cheatsheet
          Generates master SKILL.md with core mental models
               │
               ▼
          Skill written to one of:
            ~/.copilot/skills/<slug>/   (GitHub Copilot CLI)
            ~/.agents/skills/<slug>/    (Copilot CLI or Amp, cross-agent)
            ~/.claude/skills/<slug>/    (Claude Code)
          /tmp/book_skill_work/         🗑️  cleaned up

Extraction benchmark (103-page technical book, CPU only):

Method	Time	Tokens	Tables	Code blocks
pdftotext	0.1s	27K	0	0
Docling	164s	27K (+1.2%)	48	36

Real conversions (measured: pages, extracted tokens, chapters auto-detected, estimated one-pass cost on Claude Sonnet 4.5 at \$3/\$15 per MTok):

Book	Format	Pages	Tokens	Chapters	~Cost
Think Python 2	PDF	244	119K	19	\$0.88
Working Backwards	PDF	371	175K	10	\$0.96
Pro Git	PDF	501	229K	— †	\$1.23
Moby-Dick	EPUB	—	301K	— †	\$1.42

† Chapter auto-detection needs explicit Chapter N / Capítulo N headings. Pro Git uses section titles and Moby-Dick uses chapter titles / roman numerals, so neither auto-segments — extraction and conversion still work, but you point at sections manually. A full skill costs roughly \$1 per book; far less than re-reading the PDF every session.

<details> <summary>Design principles (click to expand)</summary>

Density over completeness — a 1,000-token summary beats a 10,000-token excerpt
Practitioner voice — "Use X when Y", not "The book explains X"
Front-loaded SKILL.md — compaction keeps the first ~5,000 tokens; the most important content comes first
On-demand chapters — the topic index tells Claude which file to read; chapters load only when needed
Never raw text — always synthesize, summarize, extract signal from the source

</details>

---

🧾 The Discovery Loop Tax

A PDF-reading agent doesn't just read — it navigates. Ask it one question and it fetches the table of contents, notices a term it can't define, pulls more pages, backtracks. Every one of those hops lands in the conversation history and gets re-processed on every subsequent turn. To stay inside its budget, a sub-agent is then forced to compress what it read at brutal ratios, handing the main agent a degraded summary it can't fact-check against the source.

book-to-skill pays the navigation cost once, at compile time. At runtime the assistant loads a small resident core plus the one pre-compiled chapter it needs — no discovery loop, no compress-to-fit, and the full extracted source stays on disk for verification.

Measured, not asserted. Running tools/discovery_tax.py on three real books — tokens entering context to answer a single targeted question (book-to-skill = resident core + one compiled chapter ≈ 5,000 tokens):

Book (size)	Context-dump	Discovery loop	book-to-skill	vs dump / loop
Think Python 2 (119K, small chapters)	119,264	12,152	~5,000	24× / 2.4×
Working Backwards (175K, medium chapters)	175,253	33,444	~5,000	35× / 6.7×
AI Engineering (256K, large chapters)	256,287	77,866	~5,000	51× / 15.6×

The advantage scales with chapter size: against a context-dump it's consistently 24–51× (and that cost recurs every turn); against a one-time discovery loop it ranges from a modest 2.4× on a book of small chapters to 15.6× on one of large chapters. Reproduce on your own book:

python3 tools/discovery_tax.py --full-text /tmp/book_skill_work/full_text.txt --target-chapter 5

Honest caveats: (1) the discovery figures are a one-time cost and a model using the book's real ToC/chapter sizes — a well-tuned agent lands nearer the best case; the context-dump cost, by contrast, recurs on every turn. (2) The tool needs explicit Chapter N / Capítulo N headings to segment a book; titles-only or roman-numeral books (and EPUBs extracted without ebooklib) won't segment cleanly. book-to-skill wins when you return to the knowledge repeatedly; for a single one-off read, a plain PDF agent is fine.

---

❓ FAQ

"Can't I just dump the PDF/EPUB into my Claude project context?"

You can — but every conversation will burn that token budget upfront. A 400-page book is ~200K tokens. With a skill, only the chapters relevant to your question load — typically a SKILL.md core (~4K) plus the one chapter you asked about (~1K). The rest stays on disk until you need it.

The economics are amortization, not size. Pasting the book pays the full token bill on every turn of every session, forever. book-to-skill pays the extraction cost once and every future conversation loads only the slice it needs. The bigger your context window, the more this matters — a large window makes the dump possible, not cheap.

More importantly: raw text injection is retrieval. A skill is reasoning. When you load a chapter file, Claude isn't searching for keyword matches — it's working with pre-extracted named frameworks, principles, and mental models structured for application, not for reading.

---

"Claude has a 1M-token context window now — can't I just keep the whole book loaded?"

A bigger window changes what fits, not what's smart. Three reasons it isn't a substitute:

You pay per token, per call. A 1M window doesn't make those tokens free — it makes a large, recurring bill possible. The skill loads kilobytes, not megabytes.
Recall degrades with fill. Models lose precision retrieving a specific fact buried in a near-full context ("lost in the middle"). A 1K curated chapter beats 200K of raw prose for answering one question.
Window ≠ structure. A full book in context is still raw text the model must re-parse every turn. The skill ships pre-extracted frameworks — reasoning, not retrieval.

Use the big window for what it's good at: a one-off pass over material you'll never need again. Use a skill for knowledge you'll reach for repeatedly.

---

"Isn't this just RAG?"

RAG works at query time: chunk the book → embed everything → find similar vectors → inject into prompt. It's optimized for "find me the part that talks about X."

book-to-skill works at compile time: one deep analysis run extracts the author's actual frameworks, names them, describes when to use each, captures the anti-patterns. The output is structure the author spent years building — not a similarity search over their sentences.

RAG answers: "here are chunks close to your query." A skill answers: "here are the 12 frameworks this author built, ready to reason with."

Pick by shape of the job:

Wide and shallow — a library of dozens of books, "find the part that mentions X" → a RAG tool (e.g. CandleKeep) wins.
Narrow and deep — one book or a tight cluster of related sources, frameworks you apply while you work → book-to-skill wins.

They're complementary, not competing: RAG indexes a shelf, book-to-skill masters a spine.

---

"Popular books are already in Claude's training data. Why bother?"

For widely-known books (Clean Code, DDIA, Pragmatic Programmer), Claude has general knowledge — but it's compressed, averaged across the entire internet's discussion of the book, and may hallucinate specific quotes or chapter locations.

book-to-skill works from your actual copy. Every framework name, every anti-pattern list, every chapter number is grounded in the text you provided. No training data drift, no hallucinated chapter titles.

It also shines for books Claude doesn't know at all: niche technical references, internal company documentation, recent publications, translated works.

---

"NotebookLM handles multiple books better."

Absolutely true — if your workflow is "I have 80 separate books and I want to search across all of them," NotebookLM is the right tool.

book-to-skill is built for a different job: you want to go deep on a specific topic or library, having multiple related documents (papers, chapters, notes) folded into a single unified skill, and even updating it over time as new material arrives! This integrates your customized knowledge base right into your coding or writing workflow, rather than in a separate browser tab.

---

📥 Install

Two ways to use it, do not confuse them: - As an agent skill (the /book-to-skill command in Claude Code, Copilot CLI, or Amp) → git clone into your skills folder (below). This is what gives you the slash command and the full convert-a-book flow. - As a standalone CLI (just the text extractor) → pip install book-to-skill, then book-to-skill --help. This does not register the agent skill; it only installs the extraction engine. See the CLI section.

The skill follows the open Agent Skills standard, so a single install works for any compatible host.

GitHub Copilot CLI (personal skill):

git clone https://github.com/virgiliojr94/book-to-skill.git ~/.copilot/skills/book-to-skill
# then, in a `copilot` session:
/skills reload
/skills info book-to-skill

Or the cross-agent path that Copilot CLI and Amp both discover:

git clone https://github.com/virgiliojr94/book-to-skill.git ~/.agents/skills/book-to-skill

Claude Code:

Copy this into your Claude Code session:

Install book-to-skill: https://raw.githubusercontent.com/virgiliojr94/book-to-skill/master/SKILL.md

Or manually using standard git clone (ensures modular engine files are fetched correctly):

git clone https://github.com/virgiliojr94/book-to-skill.git ~/.claude/skills/book-to-skill

Then in any agent session:

/book-to-skill ~/path/to/your-book.pdf
# or
/book-to-skill ~/path/to/your-book.epub

Standalone CLI (pip)

pip install book-to-skill is a separate, optional path. It installs only the text-extraction engine as a CLI, for scripting or to grab the optional extractors; it does not register the /book-to-skill agent skill (use the git clone above for that).

pip install "book-to-skill[pdf,epub,docx]"   # engine + optional extractors
book-to-skill ~/path/to/book.pdf --mode text  # or: python -m book_to_skill ...
book-to-skill --check                          # report which extractors are installed

---

📁 Repository structure

book-to-skill/
├── SKILL.md              # Skill definition + step-by-step instructions (the generator spec)
├── scripts/
│   ├── extract.py        # Thin entrypoint wrapper
│   └── extractor/        # Modular extraction package
│       ├── config.py     # Extensions, paths, dependency constants
│       ├── dependencies.py  # optional-dep probing + --check
│       ├── exceptions.py # ExtractionError (per-source failures, batch-safe)
│       ├── utils.py      # CLI parsing, multi-source resolution, chapter detection, runner
│       └── parsers/      # Format-specific parsers (pdf, epub, docx, html, rtf, calibre, text)
├── tools/
│   ├── discovery_tax.py  # measures token cost vs context-dump / discovery loop
│   └── validate_skill.py # checks a generated SKILL.md against host rules (--lens claude|copilot|amp)
├── tests/                # pytest suite (extraction, detection, discovery tax)
├── docs/
│   ├── PERFORMANCE.md    # measured benchmarks, discovery tax, cost
│   └── ARCHITECTURE.md   # pipeline + component map
├── CHANGELOG.md          # release history (semver)
├── CONTRIBUTING.md       # dev setup, PR conventions, release process
├── SECURITY.md           # vulnerability reporting
└── README.md             # This file

---

⚖️ Copyright & fair use

book-to-skill ships no book content — not a single page. It's a converter you point at files you already own.

Processing is local. Extraction and analysis run on your machine. Your files are never uploaded by this tool. (If your agent's model runs in the cloud, the text you feed it follows that provider's normal data terms — same as any prompt.)
You use your own copy. Bring a book you bought, docs your company owns, or papers you have the right to read.
The output is your notes. A generated skill is a structured, synthesized derivative — framework names, definitions, takeaways — not a reproduction of the text. The skill explicitly never copies raw passages (see Quality Rule #7). Treat it like handwritten study notes: yours, for personal use.
Don't redistribute. Publishing or sharing a generated skill of a copyrighted work can infringe the rights holder. Keep skills of third-party books private. Internal docs, your own writing, and openly-licensed material are fine to share within the bounds of their license.

When in doubt, follow the license or terms of the source document. This project is a tool; how you use it is on you.

---

💖 Sponsors

book-to-skill is free and MIT-licensed, maintained on personal time. If it saves you tokens or study hours, consider sponsoring its upkeep: PR reviews, multilingual fixes, releases, and docs.

Become a sponsor → github.com/sponsors/virgiliojr94

Every sponsor is listed in BACKERS.md. Thank you for keeping open, privacy-first tooling alive. ✨

License

MIT — applies to the converter (code + skill definition) in this repository, not to any book or document you process with it.

Star History

安装

选择适合你当前 Agent 的方式

01 · 发给当前 Agent自动识别兼容方式与安装位置

请帮我安装「book-to-skill」。它是一个 Agent Skill 或仓库级 Skill 包。
来源：https://github.com/virgiliojr94/book-to-skill

请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/virgiliojr94/book-to-skill」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。

在 GitHub 查看原文

本站按仓库级 package 收录，安装时克隆完整 book-to-skill 仓库；已核验的 skill 入口为 SKILL.md。

运行前阅读 README、依赖说明和 MIT 许可；涉及账号、外部写入或高风险领域时保留人工确认。

安装即视为你已阅读并遵守该 skill 的开源协议。内容版权归原作者所有。