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Found 15 Skills
Alembic migration patterns for SQLAlchemy 2.0 async. Use when creating database migrations, managing schema versions, handling zero-downtime deployments, or implementing reversible database changes.
SQLAlchemy ORM and Alembic migration best practices for building safe, performant database schemas. This skill should be used when writing, reviewing, or refactoring SQLAlchemy models, Alembic migrations, or database query patterns. Triggers on tasks involving SQLAlchemy ORM, Alembic migrations, database schema changes, or query optimization.
Expert guidance for SQLAlchemy 2.0 + Pydantic + PostgreSQL. Use when setting up database layers, defining models, creating migrations, or any database-related work. Automatically activated for DB tasks.
FastAPI with PostgreSQL, async SQLAlchemy 2.0, Alembic, and Docker.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
PostgreSQL best practices: multi-tenancy with RLS, schema design, Alembic migrations, async SQLAlchemy, and query optimization.
Advanced SQLModel patterns and comprehensive database migrations with Alembic. Use when creating SQLModel models, defining relationships (one-to-many, many-to-many, self-referential), setting up database migrations, optimizing queries, solving N+1 problems, implementing inheritance patterns, working with composite keys, creating indexes, performing data migrations, or troubleshooting Alembic issues. Triggers include "SQLModel", "Alembic migration", "database model", "relationship", "foreign key", "migration", "N+1 query", "query optimization", "database schema", or questions about ORM patterns.
Reviews SQLAlchemy code for session management, relationships, N+1 queries, and migration patterns. Use when reviewing SQLAlchemy 2.0 code, checking session lifecycle, relationship() usage, or Alembic migrations.
Comprehensive Alembic database migration management for customer support systems
Expert guidance for SQLModel - the Python library combining SQLAlchemy and Pydantic for database models. Use when (1) creating database models that work as both SQLAlchemy ORM and Pydantic schemas, (2) building FastAPI apps with database integration, (3) defining model relationships (one-to-many, many-to-many), (4) performing CRUD operations with type safety, (5) setting up async database sessions, (6) integrating with Alembic migrations, (7) handling model inheritance and mixins, or (8) converting between database models and API schemas.
Python backend implementation patterns for FastAPI applications with SQLAlchemy 2.0, Pydantic v2, and async patterns. Use during the implementation phase when creating or modifying FastAPI endpoints, Pydantic models, SQLAlchemy models, service layers, or repository classes. Covers async session management, dependency injection via Depends(), layered error handling, and Alembic migrations. Does NOT cover testing (use pytest-patterns), deployment (use deployment-pipeline), or FastAPI framework mechanics like middleware and WebSockets (use fastapi-patterns).
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.