moai-platform-supabase: Supabase Platform Specialist

Quick Reference (30 seconds)

Supabase Full-Stack Platform: PostgreSQL 16 with pgvector for AI/vector search, Row-Level Security for multi-tenant apps, real-time subscriptions, Edge Functions with Deno runtime, and integrated Storage with transformations.

Core Capabilities

PostgreSQL 16: Latest PostgreSQL with full SQL support, JSONB, and advanced features pgvector Extension: AI embeddings storage with HNSW/IVFFlat indexes for similarity search Row-Level Security: Automatic multi-tenant data isolation at database level Real-time Subscriptions: Live data sync via Postgres Changes and Presence Edge Functions: Serverless Deno functions at the edge Storage: File storage with automatic image transformations Auth: Built-in authentication with JWT integration

When to Use Supabase

• Multi-tenant SaaS applications requiring data isolation
• AI/ML applications needing vector embeddings and similarity search
• Real-time collaborative features (presence, live updates)
• Full-stack applications needing auth, database, and storage
• Projects requiring PostgreSQL-specific features

Context7 Documentation Access

For latest Supabase API documentation, use the Context7 MCP tools:

Step 1 - Resolve library ID: Use mcp__context7__resolve-library-id with query "supabase" to get the Context7-compatible library ID

Step 2 - Fetch documentation: Use mcp__context7__get-library-docs with the resolved library ID, specifying topic and token allocation

Example topics: "postgresql pgvector", "row-level-security policies", "realtime subscriptions presence", "edge-functions deno", "storage transformations", "auth jwt"

Module Index

This skill uses progressive disclosure with specialized modules for detailed implementation patterns.

Core Modules

postgresql-pgvector - PostgreSQL 16 with pgvector extension for AI embeddings and semantic search

• Vector storage with 1536-dimension OpenAI embeddings
• HNSW and IVFFlat index strategies
• Semantic search functions
• Hybrid search combining vector and full-text

row-level-security - RLS policies for multi-tenant data isolation

• Basic tenant isolation patterns
• Hierarchical organization access
• Role-based modification policies
• Service role bypass for server operations

realtime-presence - Real-time subscriptions and presence tracking

• Postgres Changes subscription patterns
• Filtered change listeners
• Presence state management
• Collaborative cursor and typing indicators

edge-functions - Serverless Deno functions at the edge

• Basic Edge Function with authentication
• CORS header configuration
• JWT token verification
• Rate limiting implementation

storage-cdn - File storage with image transformations

• File upload patterns
• Image transformation URLs
• Thumbnail generation
• Cache control configuration

auth-integration - Authentication patterns and JWT handling

• Server-side client creation
• Cookie-based session management
• Auth state synchronization
• Protected route patterns

typescript-patterns - TypeScript client patterns and service layers

• Server-side client for Next.js App Router
• Service layer abstraction pattern
• Subscription management
• Type-safe database operations

Quick Start Patterns

Database Setup

Enable pgvector extension and create embeddings table:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  content TEXT NOT NULL,
  embedding vector(1536),
  metadata JSONB DEFAULT '{}',
  created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_documents_embedding ON documents
USING hnsw (embedding vector_cosine_ops);

Basic RLS Policy

ALTER TABLE projects ENABLE ROW LEVEL SECURITY;

CREATE POLICY "tenant_isolation" ON projects FOR ALL
  USING (tenant_id = (auth.jwt() ->> 'tenant_id')::UUID);

Real-time Subscription

const channel = supabase.channel('db-changes')
  .on('postgres_changes',
    { event: '*', schema: 'public', table: 'messages' },
    (payload) => console.log('Change:', payload)
  )
  .subscribe()

Edge Function Template

import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'
import { createClient } from 'https://esm.sh/@supabase/supabase-js@2'

serve(async (req) => {
  const supabase = createClient(
    Deno.env.get('SUPABASE_URL')!,
    Deno.env.get('SUPABASE_SERVICE_ROLE_KEY')!
  )
  // Process request
  return new Response(JSON.stringify({ success: true }))
})

Best Practices

Performance: Use HNSW indexes for vectors, Supavisor for connection pooling in serverless Security: Always enable RLS, verify JWT tokens, use service_role only in Edge Functions Migration: Use Supabase CLI (supabase migration new, supabase db push)

Works Well With

• moai-platform-neon - Alternative PostgreSQL for specific use cases
• moai-lang-typescript - TypeScript patterns for Supabase client
• moai-domain-backend - Backend architecture integration
• moai-foundation-quality - Security and RLS best practices
• moai-workflow-testing - Test-driven development with Supabase

Module References

For detailed implementation patterns, see the modules directory:

• modules/postgresql-pgvector.md - Complete vector search implementation
• modules/row-level-security.md - Multi-tenant RLS patterns
• modules/realtime-presence.md - Real-time collaboration features
• modules/edge-functions.md - Serverless function patterns
• modules/storage-cdn.md - File storage and transformations
• modules/auth-integration.md - Authentication patterns
• modules/typescript-patterns.md - TypeScript client architecture

For API reference summary, see reference.md For full-stack templates, see examples.md

Status: Production Ready Generated with: MoAI-ADK Skill Factory v2.0 Last Updated: 2026-01-06 Version: 2.0.0 (Modularized) Coverage: PostgreSQL 16, pgvector, RLS, Real-time, Edge Functions, Storage