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Found 260 Skills
Creates a complete Amazon Aurora database cluster with instances, handling cluster creation, instance provisioning, and Secrets Manager password management in the proper sequence. Use when setting up new Aurora MySQL or PostgreSQL clusters with production-ready configuration.
DuckDB SQL reference for MotherDuck. Use when you need exact DuckDB syntax, function behavior, supported MotherDuck SQL features, or to resolve whether PostgreSQL-oriented SQL will fail on MotherDuck.
World-class database schema design - data modeling, migrations, relationships, and the battle scars from scaling databases that store billions of rowsUse when "database schema, data model, migration, prisma schema, drizzle schema, create table, add column, foreign key, primary key, uuid, auto increment, soft delete, normalization, denormalization, one to many, many to many, junction table, polymorphic, enum type, index strategy, database, schema, migration, data-model, prisma, drizzle, typeorm, postgresql, mysql, sqlite" mentioned.
Patterns and best practices for using Lakebase Autoscaling (next-gen managed PostgreSQL) with autoscaling, branching, scale-to-zero, and instant restore.
Drizzle ORM — type-safe, lightweight TypeScript ORM for SQL databases. Schema declaration, CRUD queries, joins, relations, migrations with Drizzle Kit, and database seeding. Use when defining database schemas, writing queries (select/insert/update/delete), setting up migrations, configuring drizzle.config.ts, establishing database connections, validating data with drizzle-zod/valibot, or working with PostgreSQL, MySQL, SQLite, Turso, Bun SQL, Neon, Supabase, PGlite, Expo SQLite, Cloudflare D1, PlanetScale, SingleStore, MSSQL, CockroachDB.
Specialized skill for working with Supabase PostgreSQL database including queries, RLS policies, migrations, functions, and data operations. Use when implementing database queries, creating migrations, setting up RLS policies, writing SQL functions, or debugging database issues.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
Guides the agent through async database integration with SQLAlchemy and Alembic migrations for FastAPI applications. Triggered when users ask to "set up a database", "create database models", "add SQLAlchemy", "create migrations", "run Alembic", "connect to PostgreSQL", "add a database layer", "create CRUD operations", "set up async database", or mention SQLAlchemy, Alembic, ORM, database models, async database, connection pool, or database migrations.
Build production-grade FastAPI backends with SQLModel, Dapr integration, and JWT authentication. Use when building REST APIs with Neon PostgreSQL, implementing event-driven microservices with Dapr pub/sub, scheduling jobs, or creating CRUD endpoints with JWT/JWKS verification. NOT when building simple scripts or non-microservice architectures.
Database operations including querying, schema exploration, and data analysis. Activates for tasks involving PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis, Elasticsearch, or ClickHouse databases.
Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.