Conversational Reading Assistant
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About this skill
What It Solves
Reading notes often end up as scattered excerpts without a stable map of chapters, concepts, and review questions. This skill treats a book as a managed artifact under ./reading-notes/, with states like reading, inspection, output, and completed.
How It Works
- Framework first: entering a book title generates a chapter framework with chapter lists, summaries, key points, and expected excerpt counts.
- Five-dimension cards: each note captures
original,tags,deep meaning,application, andquestion, then is assigned to a chapter. - Knowledge structure: notes update a concept index and concept-link graph; chapter reviews generate cross-chapter links and open questions.
- Output and search: supports keyword, tag, chapter, and concept search, exports to
MarkdownandHTML, and runsstats.pyfor per-book or global statistics.
Boundaries
It fits public-domain reading, note consolidation, and concept mapping; it is not a replacement for academic citation, source search, or copyright-text distribution. Deletion requires confirmation, and older data imports are adapted to the v3.0 structure.
Use Cases
- While reading a public-domain book, capture original text, tags, meaning, application, and question per chapter.
- After finishing a chapter, aggregate excerpts, build concept links and cross-chapter relations, and list open questions.
- Import older notes into v3.0, auto-generate chapter frameworks and a concept index.
- Export Markdown or HTML knowledge structure and search excerpts by keyword, tag, chapter, or concept.
Best For
- Note maintainers who need scattered reading excerpts organized into chapter frameworks and concept indexes
- Lifelong learners reading public-domain classics and building concept-link graphs across chapters
- Knowledge managers migrating older notes to v3.0 and exporting Markdown or HTML reports
- Researchers reviewing chapters to generate cross-chapter links and open questions
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