virgiliojr94/book-to-skill
book-to-skill compiles books and document sets into on-demand agent skills, reducing repeated context loading while preserving chapter-level references and reusable decision rules.
book-to-skill converts PDFs, ebooks, document folders, or globs into a shared SKILL.md, a core index, and on-demand chapter files. Its tests report **24–51× fewer tokens** than repeatedly placing a full book in context.
Use this pattern for references you revisit: internal docs, standards, research clusters, or technical books. The generated structure front-loads mental models and loads only the relevant chapter, while the same skill format works in Claude Code, Amp, and GitHub Copilot CLI.
book-to-skill converts PDFs, ebooks, document folders, or globs into a shared SKILL.md, a core index, and on-demand chapter files. Its tests report **24–51× fewer tokens** than repeatedly placing a full book in context. Use this pattern for references you revisit: internal docs, standards, research clusters, or technical books. The generated structure front-loads mental models and loads only the relevant chapter, while the same skill format works in Claude Code, Amp, and GitHub Copilot CLI. The repository says conversion costs roughly **$1 per book**, and repeated use is where amortization helps. Chapter detection expects explicit numbered headings; one-off reading, broad library search, and poorly segmented sources may be better served by other approaches.
book-to-skill makes progressive disclosure concrete for stable, repeatedly consulted documents: preserve a shared mental model and index, then load chapters on demand. Its reported token reduction supports this narrow reuse case, but does not overturn evidence that full cached history can win for conversational recall. Conversion cost, heading requirements, and weak support for one-off or cross-library search define when richer retrieval or raw-history baselines remain preferable.