FAQ
❓ 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.