deep-learning

To build a Zettelkasten knowledge network from long texts, you structure content into connected atomic, structure, method, and index notes. This skill processes books and reports end-to-end using the Luhmann Scan to generate a navigable, linked reading sequence.

The Luhmann Scan is a deep reading technique that generates and links notes sequentially during reading to form a networked reading order. It helps extract core arguments and concepts, transforming linear texts into a connected knowledge network of structure and atomic notes.

Creating atomic notes captures single concepts with definitions and context, while structure notes extract core arguments and argument maps. This skill automates that extraction from long-form articles, linking them together to support comprehensive digestion and knowledge grafting.

Yes, you can use this Zettelkasten method for books and reports without extra dependencies. It requires only frontmatter in SKILL.md with a name and description, using built-in templates and references to generate the full deep-reading workflow.

The best way to generate index notes is to create multi-entry pathways that serve as navigation points for your knowledge network. This skill automatically produces these entry points alongside method notes and structure notes, ensuring all connected concepts remain easily accessible.

Use method notes when you need actionable outputs like SOPs, templates, and checklists, whereas atomic notes capture static concepts, definitions, and mechanisms. This skill generates both types to support comprehensive digestion and practical application of your readings.