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supabase-access

Query ontology entities, embeddings, and facts from Supabase.

Updated Aug 27, 2026
Search Tags:#ontology#embeddings#supabase#vector-search#entity-management#fact-storage
One-click install
npx skills add https://github.com/GetFresh-Ventures/gxd-ceo-ai-kit --skill supabase-access
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: supabase-access
Source: https://github.com/GetFresh-Ventures/gxd-ceo-ai-kit/tree/main/skills/supabase-access
Command: npx skills add https://github.com/GetFresh-Ventures/gxd-ceo-ai-kit --skill supabase-access

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables programmatic access to Supabase for querying ontology entities, embeddings, vector searches, and fact storage, reducing manual data retrieval for AI workflows.

Core Features & Use Cases

  • Unified data access: query ontology entities, embeddings, and facts from a single backend.
  • Deterministic read/write patterns: follow defined tables and helper scripts for consistent operations.
  • Scalable governance: supports pagination and structured fact storage with clear schemas.
  • Use Case: AI agents retrieve entity dossiers, perform similarity searches, and persist new facts to the ontology.

Quick Start

Connect to your Supabase instance and begin querying ontology data, embeddings, and facts with the included helper scripts.

Frequently Asked Questions about supabase-access

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

FAQPage Schema
How do I query ontology entities and facts from Supabase for AI workflows?▼

To query ontology entities and facts from Supabase, use deterministic read patterns against defined tables. The skill surfaces connection details and leverages helper scripts to retrieve structured data, ensuring consistent programmatic access for AI agents.

What is vector search and how do embeddings work with Supabase data?▼

Vector search uses embeddings stored in Supabase to perform similarity searches. The skill enforces schema references like PIL-SCHEMA.md to ensure structured data access, allowing AI agents to retrieve entity dossiers and match similar data points accurately.

Can I persist new facts to an ontology database using Supabase?▼

Yes, you can persist new facts to an ontology database using deterministic write patterns. The skill supports structured fact storage with clear schemas and pagination, enforcing governance and security guidance to maintain data consistency across defined tables.

Does supabase-access work without external dependencies for entity management?▼

supabase-access operates without external dependencies, relying on helper scripts and schema references like PIL-SCHEMA.md for entity management. It provides unified data access to query embeddings, facts, and ontology entities directly from a single backend.

What are the limitations of using Supabase for structured reasoning in AI agents?▼

Limitations include the strict enforcement of schema references such as PIL-SCHEMA.md, meaning operations must follow deterministic read and write patterns against defined tables. This approach ensures governance but requires strict adherence to schema guidance for consistency.

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