knowledge-extraction

What problem does it solve?

Knowledge-extraction eliminates the manual effort of summarizing long-form sources into reusable claims and spaced-repetition flashcards, so you retain what you read and can review it later.

Core Features & Use Cases

• Two-phase extraction: captures key claims with citations and confidence, then converts them into Anki or Mochi-style flashcards.
• Frameworks and takeaways: produces named mental models, actionable steps, open questions, and follow-up reads from a source.
• Operator-private storage: writes outputs to ~/.config/walter-os/state/knowledge/YYYY-MM/<source-slug>.md for manual import into spaced-repetition tools.
• Use case: After finishing a book chapter, generate a structured claims document and then turn those claims into Q/A cards for ongoing review.

Quick Start

Use knowledge-extraction to extract and structure claims from a paper by providing your source text and metadata, then run a second pass to convert the extraction into Anki or Mochi cards.