supabase

Add a specific feature to an existing Supabase project. Features include table, auth, rls, realtime, storage, migration, ai.

**Project Name:** `$0`

AI feature implementation - pgvector vector search, embeddings storage, hybrid search, Edge Functions integration, and RAG system architecture

Database schema design for AI applications - optimal table structures, relationships, and indexes for chat/RAG/multi-tenant apps

Build complete auth UI pages - sign in, sign up, forgot password, email verification, profile pages for Next.js, SvelteKit, or React

SQL code and migration review - validates syntax, checks best practices, ensures performance optimization

Direct database operations via MCP - executes SQL safely, handles transactions, validates syntax, manages connections

Database migration management via MCP - versioning, safe schema changes, rollbacks, and migration history tracking

Performance analysis - query optimization, index recommendations, query plan analysis, bottleneck identification

Project configuration management via MCP - creates projects, configures settings, manages organizations

Realtime feature implementation - subscriptions, presence tracking, broadcast messaging for AI applications

Schema validation before deployment - SQL syntax, naming conventions, constraints, indexes, and RLS policy checks

Security validation - RLS policy audits, authentication setup validation, vulnerability checks

Authentication and RLS implementation - OAuth providers, Row Level Security policies, user permissions, MFA, enterprise SSO

End-to-end testing - comprehensive testing workflows for database, auth, realtime, and AI features

UI component integration - pre-built React components for auth, realtime, file upload, frontend-backend wiring

Setup and configuration validation - project setup, MCP connectivity, environment config, deployment readiness

Agentic Platform Contract tables for AI agent applications - runs, events, artifacts, and tool calls tracking. Use when building AI agent backends that need structured run/event storage.

Configure Supabase authentication providers (OAuth, JWT, email). Use when setting up authentication, configuring OAuth providers (Google/GitHub/Discord), implementing auth flows, configuring JWT settings, or when user mentions Supabase auth, social login, authentication setup, or auth configuration.

End-to-end testing scenarios for Supabase - complete workflow tests from project creation to AI features, validation scripts, and comprehensive test suites. Use when testing Supabase integrations, validating AI workflows, running E2E tests, verifying production readiness, or when user mentions Supabase testing, E2E tests, integration testing, pgvector testing, auth testing, or test automation.

Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.

Row Level Security policy templates for Supabase - multi-tenant patterns, user isolation, role-based access, and secure-by-default configurations. Use when securing Supabase tables, implementing RLS policies, building multi-tenant AI apps, protecting user data, creating chat/RAG systems, or when user mentions row level security, RLS, Supabase security, tenant isolation, or data access policies.

RLS policy testing patterns for Supabase - automated test cases for Row Level Security enforcement, user isolation verification, multi-tenant security, and comprehensive security audit scripts. Use when testing RLS policies, validating user isolation, auditing Supabase security, verifying tenant isolation, testing row level security, running security tests, or when user mentions RLS testing, security validation, policy testing, or data leak prevention.

Production-ready database schema patterns for AI applications including chat/conversation schemas, RAG document storage with pgvector, multi-tenant organization models, user management, and AI usage tracking. Use when building AI applications, creating database schemas, setting up chat systems, implementing RAG, designing multi-tenant databases, or when user mentions supabase schemas, chat database, RAG storage, pgvector, embeddings, conversation history, or AI application database.

Database schema validation tools - SQL syntax checking, constraint validation, naming convention enforcement, and schema integrity verification. Use when validating database schemas, checking migrations, enforcing naming conventions, verifying constraints, or when user mentions schema validation, migration checks, database best practices, or PostgreSQL validation.