← Back to Skills

moai-baas-supabase-ext

modu-ai
Updated Today
18 views
424
78
424
View on GitHub
Otherai

About

This Claude Skill provides enterprise-grade Supabase PostgreSQL management with AI-powered backend-as-a-service architecture. It enables intelligent database orchestration, real-time capabilities, and Context7 integration for scalable modern applications. Use this skill when you need expert assistance with production-ready Supabase deployments, row-level security, and edge functions.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/modu-ai/moai-adk
Git CloneAlternative
git clone https://github.com/modu-ai/moai-adk.git ~/.claude/skills/moai-baas-supabase-ext

Copy and paste this command in Claude Code to install this skill

Documentation

Enterprise Supabase PostgreSQL Platform Expert v4.0.0

Skill Metadata

FieldValue
Skill Namemoai-baas-supabase-ext
Version4.0.0 (2025-11-13)
TierEnterprise Database Platform Expert
AI-Poweredβœ… Context7 Integration, Intelligent Architecture
Auto-loadOn demand when Supabase keywords detected

What It Does

Enterprise Supabase PostgreSQL Platform expert with AI-powered open-source BaaS architecture, Context7 integration, and intelligent PostgreSQL orchestration for scalable modern applications.

Revolutionary v4.0.0 capabilities:

  • πŸ€– AI-Powered Supabase Architecture using Context7 MCP for latest PostgreSQL documentation
  • πŸ“Š Intelligent Real-time Orchestration with automated subscription optimization
  • πŸš€ Advanced PostgreSQL Optimization with AI-driven query tuning and indexing
  • πŸ”— Enterprise Row-Level Security with zero-configuration data protection
  • πŸ“ˆ Predictive Performance Analytics with usage forecasting and optimization

When to Use

Automatic triggers:

  • Supabase PostgreSQL architecture and real-time feature discussions
  • Row-level security implementation and optimization
  • Edge Functions and serverless backend development
  • Open-source BaaS platform evaluation and integration

Manual invocation:

  • Designing enterprise Supabase architectures with PostgreSQL optimization
  • Implementing real-time subscriptions and data synchronization
  • Planning PostgreSQL to Supabase migrations
  • Configuring advanced security with Row-Level Security

Quick Reference (Level 1)

Supabase PostgreSQL Platform (November 2025)

Core Features Overview

  • PostgreSQL 16+: Latest version with extensions and performance improvements
  • Real-time Subscriptions: WebSocket-based real-time data synchronization
  • Row-Level Security (RLS): Fine-grained data access control
  • Edge Functions: Deno runtime with 28+ global regions
  • File Storage: Object storage with CDN integration
  • Authentication: Multi-provider auth with PostgreSQL integration

Key PostgreSQL Extensions

  • pgvector: Vector similarity search for AI applications
  • pg_cron: Scheduled database jobs and maintenance
  • PostgREST: Automatic REST API generation from PostgreSQL schemas
  • pg_graphql: GraphQL API generation from PostgreSQL

Latest Features (November 2025)

  • Database Branching: Development workflows with isolated environments
  • Improved Auth Section: User bans and authenticated logs
  • Official Vercel Integration: Seamless deployment and management
  • Enhanced Edge Functions: Improved cold start performance

Performance Characteristics

  • Query Performance: P95 < 50ms latency
  • Real-time Latency: WebSocket connections < 100ms
  • Throughput: 50k+ TPS with connection pooling
  • Global Coverage: 28+ edge regions worldwide

Core Implementation (Level 2)

Supabase Architecture Intelligence

# AI-powered Supabase architecture optimization with Context7
class SupabaseArchitectOptimizer:
    def __init__(self):
        self.context7_client = Context7Client()
        self.postgresql_analyzer = PostgreSQLAnalyzer()
        self.realtime_optimizer = RealtimeOptimizer()
    
    async def design_optimal_supabase_architecture(self, 
                                                 requirements: ApplicationRequirements) -> SupabaseArchitecture:
        """Design optimal Supabase architecture using AI analysis."""
        
        # Get latest Supabase and PostgreSQL documentation via Context7
        supabase_docs = await self.context7_client.get_library_docs(
            context7_library_id='/supabase/docs',
            topic="postgresql rls realtime edge-functions optimization 2025",
            tokens=3000
        )
        
        postgresql_docs = await self.context7_client.get_library_docs(
            context7_library_id='/postgresql/docs',
            topic="performance optimization indexing extensions 2025",
            tokens=2000
        )
        
        # Optimize PostgreSQL configuration
        db_optimization = self.postgresql_analyzer.optimize_configuration(
            requirements.database_workload,
            postgresql_docs
        )
        
        # Design real-time architecture
        realtime_design = self.realtime_optimizer.design_realtime_system(
            requirements.realtime_needs,
            supabase_docs
        )
        
        return SupabaseArchitecture(
            database_configuration=db_optimization,
            realtime_system=realtime_design,
            edge_functions=self._design_edge_functions(requirements),
            storage_configuration=self._optimize_storage(requirements),
            auth_integration=self._integrate_authentication(requirements),
            rls_policies=self._design_row_level_security(requirements),
            performance_predictions=db_optimization.predictions
        )

Row-Level Security Implementation

-- Comprehensive RLS policies for enterprise applications
-- Enable RLS on sensitive tables
ALTER TABLE profiles ENABLE ROW LEVEL SECURITY;
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;
ALTER TABLE documents ENABLE ROW LEVEL SECURITY;

-- User can only access their own profile
CREATE POLICY "Users can view own profile" ON profiles
    FOR SELECT USING (auth.uid() = user_id);

CREATE POLICY "Users can update own profile" ON profiles
    FOR UPDATE USING (auth.uid() = user_id);

-- Project access based on team membership
CREATE POLICY "Team members can view team projects" ON projects
    FOR SELECT USING (
        user_id IN (
            SELECT user_id FROM team_members 
            WHERE team_id = projects.team_id
        )
    );

CREATE POLICY "Team admins can manage team projects" ON projects
    FOR ALL USING (
        user_id IN (
            SELECT user_id FROM team_members 
            WHERE team_id = projects.team_id AND role = 'admin'
        )
    );

-- Document access with role-based permissions
CREATE POLICY "Document access control" ON documents
    FOR ALL USING (
        CASE
            WHEN is_public THEN true
            WHEN owner_id = auth.uid() THEN true
            WHEN user_id IN (
                SELECT user_id FROM document_shares
                WHERE document_id = documents.id AND permission IN ('read', 'write', 'admin')
            ) THEN true
            ELSE false
        END
    );

Real-time Subscription Patterns

// Advanced real-time subscription management
import { RealtimeChannel } from '@supabase/supabase-js';

class RealtimeManager {
  private subscriptions: Map<string, RealtimeChannel> = new Map();
  
  subscribeToDataChanges(
    table: string, 
    filter: object, 
    callback: (payload: any) => void
  ): string {
    const subscriptionId = `${table}_${Date.now()}`;
    
    const channel = supabase
      .channel(subscriptionId)
      .on(
        'postgres_changes',
        { 
          event: '*', 
          schema: 'public', 
          table: table,
          filter: filter
        },
        callback
      )
      .subscribe();
    
    this.subscriptions.set(subscriptionId, channel);
    return subscriptionId;
  }
  
  subscribeToUserSpecificData(userId: string) {
    return {
      profile: this.subscribeToDataChanges(
        'profiles',
        { user_id: `eq.${userId}` },
        (payload) => this.handleProfileUpdate(payload)
      ),
      projects: this.subscribeToDataChanges(
        'projects',
        { team_id: `in.(${this.getUserTeamIds(userId).join(',')})` },
        (payload) => this.handleProjectUpdate(payload)
      ),
      notifications: this.subscribeToDataChanges(
        'notifications',
        { user_id: `eq.${userId}` },
        (payload) => this.handleNotification(payload)
      )
    };
  }
  
  unsubscribe(subscriptionId: string) {
    const channel = this.subscriptions.get(subscriptionId);
    if (channel) {
      supabase.removeChannel(channel);
      this.subscriptions.delete(subscriptionId);
    }
  }
}

Advanced Implementation (Level 3)

Edge Functions Development

// Advanced Edge Function with TypeScript
import { serve } from 'https://deno.land/[email protected]/http/server.ts';
import { createClient } from 'https://esm.sh/@supabase/supabase-js@2';

interface NotificationPayload {
  userId: string;
  type: 'message' | 'mention' | 'project_update';
  title: string;
  body: string;
  data?: Record<string, any>;
}

serve(async (req) => {
  try {
    const { userId, type, title, body, data }: NotificationPayload = await req.json();
    
    // Initialize Supabase client
    const supabaseUrl = Deno.env.get('SUPABASE_URL')!;
    const supabaseKey = Deno.env.get('SUPABASE_SERVICE_ROLE_KEY')!;
    const supabase = createClient(supabaseUrl, supabaseKey);
    
    // Validate user exists and has notifications enabled
    const { data: user, error: userError } = await supabase
      .from('profiles')
      .select('id, notification_preferences')
      .eq('id', userId)
      .single();
    
    if (userError || !user?.notification_preferences?.[type]) {
      return new Response(JSON.stringify({ error: 'Invalid user or preferences' }), {
        status: 400,
        headers: { 'Content-Type': 'application/json' }
      });
    }
    
    // Store notification in database
    const { error: insertError } = await supabase
      .from('notifications')
      .insert({
        user_id: userId,
        type,
        title,
        body,
        data,
        created_at: new Date().toISOString()
      });
    
    if (insertError) throw insertError;
    
    // Trigger real-time notification
    const { error: realtimeError } = await supabase
      .channel('notifications')
      .send({
        type: 'broadcast',
        event: 'new_notification',
        payload: { userId, type, title, body, data }
      });
    
    return new Response(JSON.stringify({ success: true }), {
      status: 200,
      headers: { 'Content-Type': 'application/json' }
    });
    
  } catch (error) {
    console.error('Notification function error:', error);
    return new Response(JSON.stringify({ error: error.message }), {
      status: 500,
      headers: { 'Content-Type': 'application/json' }
    });
  }
});

Migration from Firebase to Supabase

class FirebaseToSupabaseMigrator:
    def __init__(self):
        self.supabase_client = SupabaseClient()
        self.firebase_client = FirebaseClient()
        self.data_transformer = DataTransformer()
    
    async def migrate_firestore_to_supabase(self, 
                                          firebase_config: FirebaseConfig,
                                          supabase_config: SupabaseConfig) -> MigrationResult:
        """Migrate Firestore collections to Supabase PostgreSQL."""
        
        # Get all Firestore collections
        collections = await self.firebase_client.list_collections()
        
        migration_results = []
        
        for collection in collections:
            # Get all documents from collection
            documents = await self.firebase_client.get_collection_documents(collection)
            
            # Transform Firebase data to PostgreSQL schema
            transformed_data = await self.data_transformer.transform_firestore_to_postgresql(
                documents, collection
            )
            
            # Create PostgreSQL table if not exists
            await self.supabase_client.create_table_from_schema(
                collection, transformed_data.schema
            )
            
            # Insert transformed data
            insert_result = await self.supabase_client.bulk_insert(
                collection, transformed_data.data
            )
            
            migration_results.append({
                collection: collection,
                documents_migrated: len(documents),
                status: insert_result.status
            })
        
        return MigrationResult(
            collections_migrated=len(collections),
            total_documents=sum(r['documents_migrated'] for r in migration_results),
            results=migration_results,
            success=True
        )

Reference & Integration (Level 4)

API Reference

Core Supabase Operations

  • create_table(schema_definition) - Create PostgreSQL table
  • enable_rls(table_name) - Enable Row-Level Security
  • create_policy(table, name, definition) - Create RLS policy
  • deploy_edge_function(name, code) - Deploy Edge Function
  • create_realtime_subscription(channel, filter) - Create real-time subscription

Context7 Integration

  • get_latest_supabase_documentation() - Official Supabase docs via Context7
  • analyze_postgresql_optimization() - PostgreSQL performance via Context7
  • optimize_realtime_architecture() - Real-time best practices via Context7

Best Practices (November 2025)

DO

  • Use Row-Level Security for all data access control
  • Implement comprehensive database indexing for performance
  • Optimize Edge Functions for cold start performance
  • Use database branching for development workflows
  • Implement connection pooling for high-throughput applications
  • Monitor real-time subscriptions for performance and cost
  • Use PostgreSQL extensions for specialized functionality
  • Implement proper backup and disaster recovery

DON'T

  • Skip RLS policies on sensitive data tables
  • Overcomplicate real-time subscriptions with excessive filters
  • Ignore Edge Function cold start optimization
  • Use production database for development testing
  • Neglect PostgreSQL query optimization
  • Forget to monitor real-time subscription costs
  • Skip database migration testing
  • Ignore security best practices for Edge Functions

Works Well With

  • moai-baas-foundation (Enterprise BaaS architecture patterns)
  • moai-domain-database (Database design and optimization)
  • moai-baas-neon-ext (PostgreSQL alternative comparison)
  • moai-essentials-perf (Performance optimization)
  • moai-security-api (API security and authorization)
  • moai-foundation-trust (Security and compliance)
  • moai-baas-firebase-ext (Firebase migration comparison)
  • moai-domain-backend (Backend database integration)

Changelog

  • v4.0.0 (2025-11-13): Complete Enterprise v4.0 rewrite with 40% content reduction, 4-layer Progressive Disclosure structure, Context7 integration, November 2025 Supabase platform updates, and advanced PostgreSQL optimization
  • v2.0.0 (2025-11-11): Complete metadata structure, RLS patterns, Edge Functions
  • v1.0.0 (2025-11-11): Initial Supabase PostgreSQL platform

End of Skill | Updated 2025-11-13

Security & Compliance

PostgreSQL Security

  • Row-Level Security for fine-grained access control
  • PostgreSQL native encryption and authentication
  • Comprehensive audit logging and monitoring
  • Advanced user privilege management

Data Protection

  • End-to-end encryption for data in transit and at rest
  • GDPR compliance with data portability and deletion
  • HIPAA ready configuration for healthcare applications
  • SOC2 Type II security controls implementation

End of Enterprise Supabase PostgreSQL Platform Expert v4.0.0

GitHub Repository

modu-ai/moai-adk
Path: src/moai_adk/templates/.claude/skills/moai-baas-supabase-ext
agentic-aiagentic-codingagentic-workflowclaudeclaudecodevibe-coding

Related Skills

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

llamaguard

Other

LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill