gbrain

Builds a PostgreSQL-native hybrid RAG knowledge brain from Markdown notes and conversations.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/escotilha/claude-public --skill gbrain
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gbrain
Source: https://github.com/escotilha/claude-public/tree/main/skills/gbrain
Command: npx skills add https://github.com/escotilha/claude-public --skill gbrain

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of scattered personal and team knowledge by compiling your notes into a searchable knowledge base that combines keyword search and semantic retrieval, so you can answer questions faster with better context.

Core Features & Use Cases

  • Hybrid RAG knowledge brain: stores compiled truth and append-only timelines in PostgreSQL to support accurate recall and change history.
  • Typed-graph entity linking: connects people, companies, concepts, and meetings with relationship types so queries stay grounded and navigable.
  • Operational workflow commands: supports setup, markdown import/sync, hybrid query, ingestion of new signals, and ongoing health/stats checks for continued quality.

Use case: You ingest meeting notes and scattered markdown files, then later ask what is known about a specific company or person and receive fused keyword+vector results grounded in stored pages and timelines.

Quick Start

Use the gbrain skill to query what you know about an entity by running the command: /gbrain query "What do we know about Company X?"

Frequently Asked Questions about gbrain

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

FAQPage Schema
How does hybrid RAG retrieval improve personal knowledge management?▼

Hybrid RAG retrieval enhances personal knowledge management by fusing tsvector keyword search with vector embeddings in Postgres, using RRF fusion to return accurate, context-rich results from compiled notes.

How do I import and sync markdown notes into a searchable knowledge base?▼

You import and sync markdown notes into a searchable knowledge base by using operational workflow commands that compile files into PostgreSQL, creating searchable pages with append-only timelines and typed entity relationships.

Does this knowledge brain require PostgreSQL to store embeddings and timelines?▼

Yes, this knowledge brain requires PostgreSQL storage to handle 1536-dimension embeddings, keyword search via tsvector, vector search, and append-only timelines for reliable hybrid RAG retrieval.

What is the best way to answer entity-focused questions from scattered meeting notes?▼

The best way to answer entity-focused questions from scattered meeting notes is using multi-query expansion and timeline-aware context against a typed entity graph, retrieving grounded facts about specific people, companies, and concepts.

Can I link people, companies, and concepts in a personal knowledge graph?▼

Yes, you can link people, companies, and concepts using typed-graph entity linking, which connects entities with specific relationship types so queries remain grounded and navigable within your knowledge base.