Store and retrieve atomic notes with semantic connections in SQLite.
npx skills add https://github.com/eddmann/jeeves --skill zettelkasten-eddmann
Please help me install this Agent Skill. Skill: zettelkasten Source: https://github.com/eddmann/jeeves/tree/main/skills/zettelkasten Command: npx skills add https://github.com/eddmann/jeeves --skill zettelkasten-eddmann
The Zettelkasten Skill helps you store atomic notes, connect ideas with links, and build a searchable personal knowledge base, making it easier to capture and retrieve knowledge over time.
Initialize the Zettelkasten database and begin creating, tagging, and linking atomic notes.
High-intent search queries and answers about installing and using this skill.
Build a personal knowledge base by storing atomic notes in a lightweight SQLite store, using full-text search and tag indexing for fast retrieval and semantic discovery. You create, update, delete, and link notes to form a navigable knowledge graph.
Semantic search for retrieving linked notes works by generating optional vector embeddings via sqlite-vec, allowing you to query the SQLite store for conceptually similar ideas rather than relying solely on exact keyword matches.
Yes, semantic search for your notes requires a SQLite database using sqlite-utils and sqlite-vec, along with the OpenAI library to generate the optional vector embeddings needed for semantic queries.
The best way to connect ideas and build a knowledge graph locally is capturing single atomic notes, then creating links between them in a SQLite database to reveal relationships and enable navigable memory recall across sessions.
Yes, you can use this knowledge management approach without vector embeddings by relying on the built-in SQLite full-text search and tag indexing to retrieve atomic notes, though you will lose semantic discovery capabilities.
malue-aiStore and search personal knowledge snippets in a SQLite FTS5 database.
loriensleafsOrganize knowledge into atomic notes with bidirectional relations in a semantic memory graph.
arunodaSearch extracted facts locally using Ollama embeddings and sqlite-vec.
dallenpyrahSearch, create, and link notes in Obsidian vaults using wikilinks.
