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sqlite-fts5-memory-db

Build local SQLite knowledge stores with FTS5 full-text search.

Updated Mar 21, 2026
Search Tags:#migrations#sqlite#schema-design#local-storage#fts5#memory-database
One-click install
npx skills add https://github.com/jflamb/project-memory --skill sqlite-fts5-memory-db
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sqlite-fts5-memory-db
Source: https://github.com/jflamb/project-memory/tree/main/.claude/skills/sqlite-fts5-memory-db
Command: npx skills add https://github.com/jflamb/project-memory --skill sqlite-fts5-memory-db

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Patterns for building local knowledge/memory databases with SQLite, focusing on full-text search with FTS5 and schemas that can evolve toward semantic search.

Core Features & Use Cases

  • Local knowledge store powered by SQLite and FTS5 with fast keyword search and ranking.
  • Flexible schema patterns designed to evolve toward embedding-enabled semantic search.
  • WAL mode, migrations without ORM, and structured document storage for AI memory.

Quick Start

Create a local memory database with SQLite and FTS5 to index documents and enable fast text search.

Frequently Asked Questions about sqlite-fts5-memory-db

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a local knowledge store with SQLite and FTS5 full-text search?▼

Create a local memory database in SQLite using FTS5 to index documents and enable fast text search. Apply schema design patterns and WAL mode for structured document storage and efficient offline retrieval.

What is the best way to handle SQLite schema migrations without an ORM for a memory database?▼

Handle SQLite migrations without an ORM by applying manual schema design patterns for structured document storage. This approach ensures the local knowledge store evolves safely while maintaining FTS5 index synchronization.

Can I evolve a SQLite FTS5 database schema toward semantic search with vector embeddings?▼

Yes, you can prepare a SQLite FTS5 database for future vector embeddings by using flexible schema patterns during initial design. These schemas ensure the local knowledge store supports both keyword search and eventual embedding-enabled semantic search.

Does SQLite FTS5 work for offline document indexing and AI memory retrieval?▼

Yes, SQLite FTS5 works for offline document indexing and AI memory retrieval by providing fast keyword search and ranking across code, notes, and knowledge bases. It stores structured documents locally without requiring external services.

How do I synchronize content between an FTS5 index and the main SQLite memory database tables?▼

Synchronize content between FTS5 and SQLite tables by applying content synchronization patterns during schema design. This ensures the local knowledge store maintains consistent search results across indexed documents and underlying storage.

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