google-agents-cli-scaffold
Project scaffolding, deployment configuration, and CI/CD setup for Google ADK agents.
moai-baas-neon-ext
Enterprise Neon Serverless PostgreSQL Platform with AI-powered database architecture, Context7 integration, and intelligent branching orchestration for scalable modern applications
Full skill instructions
| Field | Value |
|---|---|
| Skill Name | moai-baas-neon-ext |
| Version | 4.0.0 (2025-11-13) |
| Tier | Enterprise Database Platform Expert |
| AI-Powered | ✅ Context7 Integration, Intelligent Architecture |
| Auto-load | On demand when Neon keywords detected |
Enterprise Neon Serverless PostgreSQL Platform expert with AI-powered database architecture, Context7 integration, and intelligent branching orchestration for scalable modern applications.
Revolutionary v4.0.0 capabilities:
Automatic triggers:
Manual invocation:
# AI-powered Neon architecture optimization with Context7
class NeonArchitectOptimizer:
def __init__(self):
self.context7_client = Context7Client()
self.performance_analyzer = PostgreSQLAnalyzer()
self.cost_optimizer = CostOptimizer()
async def design_optimal_neon_architecture(self,
requirements: ApplicationRequirements) -> NeonArchitecture:
"""Design optimal Neon architecture using AI analysis."""
# Get latest Neon and PostgreSQL documentation via Context7
neon_docs = await self.context7_client.get_library_docs(
context7_library_id='/neon/docs',
topic="serverless architecture branching optimization performance 2025",
tokens=3000
)
postgresql_docs = await self.context7_client.get_library_docs(
context7_library_id='/postgresql/docs',
topic="performance optimization indexing scaling 2025",
tokens=2000
)
# Analyze database requirements
db_analysis = self._analyze_database_requirements(
requirements, neon_docs, postgresql_docs
)
# Optimize branching strategy
branching_strategy = self._optimize_branching_strategy(
requirements.development_team_size,
requirements.deployment_frequency,
neon_docs
)
# Calculate cost projections
cost_analysis = self.cost_optimizer.analyze_neon_costs(
requirements, branching_strategy
)
return NeonArchitecture(
compute_tier=self._select_optimal_compute_tier(requirements),
storage_configuration=self._optimize_storage_config(requirements),
branching_strategy=branching_strategy,
replication_config=self._design_replication_strategy(requirements),
connection_pooling=self._optimize_connection_pooling(requirements),
cost_projection=cost_analysis,
performance_predictions=db_analysis.predictions,
migration_plan=self._create_migration_plan(requirements)
)
neon_branching_workflow:
development_workflow:
feature_branches:
creation: "Instant branching from main/database"
isolation: "Complete environment separation"
testing: "Automated testing on feature branches"
merging: "Pr-based branch merging with conflict resolution"
staging_environment:
branch: "main/staging branch"
data: "Anonymized production data copy"
integration: "Full integration testing"
performance: "Performance benchmarking"
production_deployment:
strategy: "Blue-green deployment with Neon branching"
rollback: "Instant rollback using branch switching"
monitoring: "Real-time performance and error monitoring"
branching_optimization:
storage_efficiency:
technology: "Copy-on-write for minimal storage overhead"
compression: "Automatic compression for branch storage"
cleanup: "Automated branch lifecycle management"
performance_considerations:
read_replicas: "Dedicated read replicas for staging/testing"
connection_pooling: "Isolated connection pools per branch"
resource_isolation: "Compute isolation for development branches"
class NeonPerformanceOptimizer:
def __init__(self):
self.query_analyzer = PostgreSQLQueryAnalyzer()
self.index_advisor = PostgreSQLIndexAdvisor()
self.connection_manager = NeonConnectionManager()
async def optimize_database_performance(self,
neon_config: NeonConfiguration) -> OptimizationPlan:
"""Optimize Neon PostgreSQL performance using AI analysis."""
# Analyze current query patterns
query_analysis = await self.query_analyzer.analyze_workload(
neon_config.connection_string
)
# Recommend optimal indexes
index_recommendations = await self.index_advisor.recommend_indexes(
query_analysis.slow_queries,
neon_config.schema_definition
)
# Optimize connection pooling
connection_optimization = self.connection_manager.optimize_pooling(
neon_config.expected_connections,
neon_config.read_write_ratio
)
return OptimizationPlan(
index_changes=index_recommendations,
connection_config=connection_optimization,
query_improvements=query_analysis.optimizations,
monitoring_setup=self._setup_performance_monitoring(),
cost_impact=self._calculate_cost_impact(
index_recommendations, connection_optimization
)
)
// Neon database configuration for Next.js
import { Pool } from 'pg';
const neonPool = new Pool({
connectionString: process.env.NEON_DATABASE_URL,
ssl: { rejectUnauthorized: false },
max: 20, // Optimized for serverless
idleTimeoutMillis: 30000,
connectionTimeoutMillis: 2000,
});
export async function query(text: string, params?: any[]) {
const start = Date.now();
const res = await neonPool.query(text, params);
const duration = Date.now() - start;
// Log slow queries for optimization
if (duration > 1000) {
console.log('Slow query:', { text, duration, rows: res.rowCount });
}
return res;
}
# GitHub Actions workflow with Neon branching
name: Test with Neon Branching
on:
pull_request:
types: [opened, synchronize]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Create Neon branch
run: |
BRANCH_NAME="pr-${{ github.event.number }}"
neon branches create \
--name $BRANCH_NAME \
--parent main \
--timezone UTC
- name: Run tests on branch
env:
DATABASE_URL: ${{ secrets.NEON_DATABASE_URL }}?options=branch%3Dpr-${{ github.event.number }}
run: npm test
- name: Clean up branch
if: always()
run: neon branches delete --name pr-${{ github.event.number }}
class PostgreSQLToNeonMigrator:
def __init__(self):
self.neon_client = NeonClient()
self.migration_analyzer = MigrationAnalyzer()
async def migrate_from_postgresql(self,
source_config: PostgreSQLConfig,
neon_config: NeonConfig) -> MigrationResult:
"""Migrate from traditional PostgreSQL to Neon."""
# Analyze source database
source_analysis = await self.migration_analyzer.analyze_database(
source_config
)
# Create migration plan
migration_plan = self._create_migration_plan(source_analysis)
# Execute migration with zero downtime
migration_result = await self._execute_migration(
source_config, neon_config, migration_plan
)
return MigrationResult(
success=migration_result.success,
migrated_tables=migration_result.tables,
data_integrity_check=migration_result.integrity_check,
performance_comparison=migration_result.performance_metrics,
rollback_plan=self._create_rollback_plan()
)
create_database(name, region) - Create new Neon databasecreate_branch(parent, name) - Create database branchscale_compute(database_id, compute_units) - Scale compute resourcescreate_read_replica(database_id, region) - Create read replicapoint_in_time_restore(database_id, timestamp) - Restore to specific timeget_latest_neon_documentation() - Official Neon docs via Context7analyze_postgresql_best_practices() - PostgreSQL optimization via Context7optimize_neon_configuration() - Latest performance tuning recommendationsmoai-baas-foundation (Enterprise BaaS architecture patterns)moai-domain-database (Database design and optimization)moai-baas-supabase-ext (PostgreSQL alternative comparison)moai-essentials-perf (Performance optimization)moai-security-api (Database security patterns)moai-foundation-trust (Security and compliance)moai-baas-vercel-ext (Next.js integration)moai-baas-railway-ext (Full-stack deployment)End of Skill | Updated 2025-11-13
End of Enterprise Neon Serverless PostgreSQL Expert v4.0.0
Project scaffolding, deployment configuration, and CI/CD setup for Google ADK agents.
Set up tracing, logging, and monitoring for deployed ADK agents across Cloud Trace, BigQuery, and third-party platforms.
Enterprise Azure infrastructure architect generating Bicep or Terraform from workload descriptions.
Plan and configure production-ready Azure Kubernetes Service clusters with Day-0 and Day-1 best practices.
Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.
Web search, scraping, extraction, crawling, and monitoring via ScrapeGraph AI CLI.
Skill for working with Firebase Hosting (Classic). Use this when you want to deploy static web apps, Single Page Apps (SPAs), or simple microservices. Do NOT use for Firebase App Hosting.
Deploy and manage web apps with Firebase App Hosting. Use this skill when deploying Next.js/Angular apps with backends.
Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
Build SEO-optimized pages at scale using templates, data, and proven playbook patterns.
Design and build isolated, reusable Convex backend components with clear boundaries and app-facing wrappers.
Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. "deploy to vercel", "set up vercel", "add environment variables to vercel".
Opus.ai: Revolutionize Your Web Experience
Build a no-code AI app in minutes.
An IDE for code migration from legacy to modern frameworks through coding agents.
Automate CGI animation in live-action scenes
Branded artistic QR-code concepts
Launch a website in seconds with AI.
Streamline Your Coding Experience with AI Code Helper
Convert any screenshot or design to clean code.