'sqlalchemy' 검색 결과

Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.

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Flask-specific delta on top of python-foundation:sqlalchemy-patterns: Flask-SQLAlchemy extension setup, db.Model declarative models (3.x Mapped style and 2.x legacy db.Column), synchronous db.session lifecycle bound to the app context, Flask-Migrate integration. Used by flask-architect (model definitions) and flask-migrate-specialist (column finalization and migration). Activated automatically by flask-plugin/stack.md. Use this skill to: - Set up the SQLAlchemy and Migrate extensions with the app-factory pattern. - Write Flask-SQLAlchemy models with the db.Model base. - Query with db.session and manage the request-scoped session lifecycle. - Integrate Flask-Migrate for Alembic-based migrations managed via flask db commands. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - Flask routing and template/API patterns — see flask-plugin:flask-conventions. - Migration execution (flask db migrate, flask db upgrade) — that's flask-migrate-specialist's job.

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Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.

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• Build REST APIs with FastAPI
• Validate input data with Pydantic V2
• Implement JWT authentication and authorization

FastAPI 앱 개발과 검토에서 구조, 입력 검증, 인증, 권한 확인, 비동기 처리, 데이터베이스 업데이트, 테스트의 설계와 구현을 지원합니다.

• FastAPI 앱 구조 결정
• 입출력 데이터 형식과 검증 정비
• 인증과 작업 권한 검토

Manage database migrations for SQLAlchemy with Alembic. Use when a user asks to version database schemas, autogenerate migration scripts from models, run or roll back migrations, set up async SQLAlchemy migrations, or handle schema changes safely in production.

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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.

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FastAPI-specific delta on top of python-foundation:sqlalchemy-patterns: async engine and AsyncSession lifecycle, get_db dependency injection, async-safe lazy loading rules, async Alembic env.py wiring. Used by fastapi-architect (model definitions) and alembic-specialist (column type finalization and migration generation). Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Manage async database sessions with AsyncSession and async_sessionmaker. - Inject sessions into routes via the get_db() dependency. - Apply async-safe lazy loading (lazy="selectin" or "raise"; never sync lazy loads). - Integrate async Alembic env.py for migration autogeneration. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - FastAPI routing and Pydantic schemas — see fastapi-plugin:fastapi-conventions. - Alembic migration execution (that's alembic-specialist's job) — this skill covers definitions.

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FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Auto-activate for advanced_alchemy, alembic/, SQLAlchemyAsyncRepositoryService, SQLAlchemyAsyncConfig, repository_type, service_class, filters, or storage. Not for raw SQLAlchemy — use sqlspec.

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Build Python APIs with FastAPI using clear routes, validated inputs, and consistent error handling. It guides asynchronous database and API calls, reusable dependencies, caching, and code organization to support readable implementations and responsive services.

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• Designing API routes and input validation
• Reviewing asynchronous database and API calls
• Organizing error handling and logging

Turn plain language requirements into SQL, investigate slow queries, document database structures, and prepare migration templates with rollback plans. Support everyday database development and ORM use, where libraries connect application code to database tables.

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• Writing SQL for reports and data retrieval
• Investigating slow queries and repeated database calls
• Preparing schema changes and rollback plans

Create a Wren MDL project, a model layer for database queries, from an existing database. Discover tables and relationships, normalize column types, generate YAML files, and validate and build the project before indexing its schema.

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• Create a Wren project from an existing database
• Add selected schemas and tables as a new data source
• Define model relationships from database constraints

SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.

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Flask web framework conventions: app factory pattern with create_app(), Blueprint registration with url_prefix, MethodView for class-based API views, Marshmallow schema validation, WTForms for HTML forms, Flask-Login for session auth, flask-jwt-extended for stateless API auth, Jinja2 template conventions, error handlers, and extension initialization. Activated automatically by flask-plugin/stack.md. Use this skill to: - Structure Flask applications with the app factory and per-feature Blueprints. - Validate JSON request data with Marshmallow and HTML forms with WTForms. - Implement session-based auth with Flask-Login or token auth with flask-jwt-extended. - Render Jinja2 templates safely or return JSON responses for API mode. - Register global error handlers for consistent error responses. Do NOT use this skill for: - SQLAlchemy ORM model patterns and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Use when working with SQLAlchemy 2.0 in Python - declarative models, queries, sessions, async engines, Alembic migrations, relationship loading strategies, N+1 detection, pool exhaustion, or 2.0 migration

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Use notebook-connector's Python connection helpers for Exasol, BucketFS, SQLAlchemy, and Ibis. Covers open_pyexasol_connection, open_sqlalchemy_connection, open_ibis_connection, open_bucketfs_bucket, open_bucketfs_location, get_backend, and related helper functions.

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Backend Python Specialist IA — Expert en développement backend Python (FastAPI, Django, SQLAlchemy, Celery, async, type safety)

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Backend Python v2 — FastAPI, SQLAlchemy, async, Pydantic, deployment

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SQLAlchemy 2.x patterns for async FastAPI projects: models, sessions, queries, bulk operations, transactions, and Alembic migrations. Trigger: When writing models, queries, bulk operations, or DB-touching service code.

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Review database schema design and detect anti-patterns. Based on common RDB design knowledge and best practices. Supports Prisma, Drizzle, Rails, Django, Laravel, TypeORM, Sequelize, SQLAlchemy, and raw SQL (DDL). Use when the user says "review DB design", "check schema", "review table design", "review migration", "check for anti-patterns", "DB設計をレビューして", "スキーマを見て", "テーブル設計を確認して", "マイグレーションをレビューして", "アンチパターンがないか調べて", "この設計で問題ないか見て".

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Set up Python APIs with FastAPI using organized project templates. Build routes, asynchronous database operations, authentication, and error handling, with examples for sharing dependencies and testing endpoints through a separate test database.

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• Set up a new FastAPI project
• Build a Python API with asynchronous database access
• Organize authentication and shared dependencies

Build a tracking system for vulnerabilities that cannot be fixed on time. Define exception requests, approval workflows, alternative protective measures, expiration checks, and audit records so teams can track accepted risks and support compliance reviews.

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• Standardizing exception requests for delayed vulnerability fixes
• Recording alternative protections and risk acceptance
• Checking expired exceptions and producing monthly reports

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Answer Python questions and write code using examples fetched from pythonsheets.com. Find patterns for syntax, asynchronous tasks, networking, databases, machine learning, and distributed computing, or use topic-based questions to prepare for Python interviews.

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• Preparing for Python interviews
• Learning asyncio through examples
• Writing a socket server