Does it work with TypeScript?

Yes, it features comprehensive TypeScript client patterns and service layer abstractions for type-safe database operations and state management.

Can I use this for AI applications?

Absolutely; it includes specific implementation guides for semantic search and managing high-dimension vector embeddings for LLMs.

How does it handle security?

The skill provides patterns for Row-Level Security (RLS), JWT verification, and service role management to ensure robust data isolation.

Does this skill support the latest Supabase features?

Yes, it includes support for PostgreSQL 16, HNSW indexes via pgvector, and Deno-based Edge Functions.

Supabase Platform Specialist

Name: Supabase Platform Specialist
Author: zellycloud

byzellycloud

Database Management

Architects and implements full-stack Supabase applications using PostgreSQL 16, real-time subscriptions, and AI-powered vector search.

About

This skill transforms Claude into a Supabase expert capable of designing robust database schemas, implementing Row-Level Security (RLS) for multi-tenant isolation, and deploying serverless Edge Functions. It provides specialized knowledge for integrating pgvector for semantic search, managing real-time data synchronization via Postgres Changes, and configuring secure file storage with CDN transformations. Ideal for developers building scalable SaaS applications, AI-driven platforms, or collaborative tools that require a modern backend-as-a-service (BaaS) architecture.

Key Features

Real-time data synchronization and presence tracking patterns.
Row-Level Security (RLS) policy creation for secure multi-tenant isolation.
Storage management with automated CDN image transformations.
0 GitHub stars
PostgreSQL 16 schema design and pgvector implementation for AI search.
Deno-based Edge Function development with JWT authentication.

Use Cases

Creating real-time collaborative dashboards with live updates and presence indicators.
Developing AI-powered applications that require semantic search and vector embeddings.
Building a multi-tenant SaaS with strict data isolation and secure user auth.

About

Key Features

Real-time data synchronization and presence tracking patterns.
Row-Level Security (RLS) policy creation for secure multi-tenant isolation.
Storage management with automated CDN image transformations.
0 GitHub stars
PostgreSQL 16 schema design and pgvector implementation for AI search.
Deno-based Edge Function development with JWT authentication.

Use Cases

Creating real-time collaborative dashboards with live updates and presence indicators.
Developing AI-powered applications that require semantic search and vector embeddings.
Building a multi-tenant SaaS with strict data isolation and secure user auth.