Expert knowledge for deploying and operating Composable Rust applications in production. Use when setting up database migrations, configuring connection pools, implementing backup/restore procedures, tuning performance, setting up monitoring and observability, or handling operational concerns like disaster recovery and production database management.
Safely switch between local and production environments with validation and step-by-step guidance. Use when user wants to "switch to local", "change to production", "separate environments", or "toggle between databases". Prevents accidental production data access.
Migrate and sync vector database collections across environments
Use when creating database migrations. Prevents data loss, downtime, and performance issues. Supports PostgreSQL, MySQL, SQLite. Python 3.8+
Auto-invoke when reviewing schema design, database queries, ORM usage, or migrations. Enforces normalization, indexing awareness, query optimization, and migration safety.
Set up database replication for high availability and disaster recovery. Use when configuring master-slave replication, multi-master setups, or replication monitoring.
Analyze and optimize Django ORM queries including N+1 problems, missing indexes, slow queries, and migration issues. Use when troubleshooting slow API responses, database performance, query optimization, or migration errors.
Transform a Rails application to use Falcon web server with async job processing (async-job), async Action Cable, and Redis-compatible database (Valkey for production). Use when the user wants to add async/Falcon stack to a Rails project, migrate from Puma to Falcon, or set up async job processing with Redis for both development and production environments including Kamal deployment.
Create, manage, and safely run Ecto database migrations with proper rollback handling and best practices. Use when working with database schema changes, adding columns, or modifying constraints.
Improve database query performance through indexing, query optimization, and execution plan analysis. Reduce response times and database load.
Database schema migration patterns and best practices. Use when creating database migrations, implementing zero-downtime schema changes, version control for databases, or managing data migrations.
Health check patterns for different service types in Dokploy templates. Covers HTTP, PostgreSQL, MongoDB, Redis, MySQL, and custom health checks.
PostgreSQL database operations specialist using Drizzle ORM for schema management, queries, and migrations
Type-safe database ORM for TypeScript/Node.js. Use when you need database access with full TypeScript integration — auto-generated types from schema, migrations, and query builder. Best for PostgreSQL, MySQL, SQLite, MongoDB. Choose Prisma over raw SQL or Knex when type safety and developer experience are priorities.
Go database operations - SQL, ORMs, transactions, migrations
Apply Grey Haven database conventions - snake_case fields, multi-tenant with tenant_id and RLS, proper indexing, migrations for Drizzle (TypeScript) and SQLModel (Python). Use when designing schemas, writing database code, creating migrations, setting up RLS policies, or when user mentions 'database', 'schema', 'Drizzle', 'SQLModel', 'migration', 'RLS', 'tenant_id', 'snake_case', 'indexes', or 'foreign keys'.
Use when writing BigQuery queries, optimizing query performance, analyzing execution plans, or avoiding common SQL gotchas. Covers parameterized queries, UDFs, scripting, window functions (QUALIFY, ROW_NUMBER, RANK, LEAD/LAG), JSON functions, ARRAY/STRUCT operations, BigQuery-specific features (EXCEPT, REPLACE, SAFE_*), CTE re-execution issues, NOT IN with NULLs, DML performance, Standard vs Legacy SQL, and performance best practices.
SQLite database for Israeli Tech Nano-Influencers with direct sqlite3 command-line access. Execute any SQL query or inspect schema. Simple and agent-friendly.
JOOQ type-safe SQL patterns - use for database queries, repositories, complex SQL operations, and PostgreSQL-specific features
Specialized skill for designing AWS DynamoDB single-table schemas with optimized access patterns. Use when modeling data, designing table structure, or optimizing DynamoDB queries for production applications.
