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Found 38 Skills
Use when scaffolding production-ready FastAPI services with uv, SQLAlchemy, Alembic, Postgres, Docker, and CI gates.
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.
PostgreSQL best practices: multi-tenancy with RLS, schema design, Alembic migrations, async SQLAlchemy, and query optimization.
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.
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.
Develop lightweight Flask APIs with routing, blueprints, database integration, authentication, and request/response handling. Use when building RESTful APIs, microservices, or lightweight web services with Flask.
Build high-performance FastAPI applications with async routes, validation, dependency injection, security, and automatic API documentation. Use when developing modern Python APIs with async support, automatic OpenAPI documentation, and high performance requirements.
Comprehensive Alembic database migration management for customer support systems
Flask - Lightweight Python web framework for microservices, REST APIs, and flexible web applications with extensive extension ecosystem
This skill should be used when the user asks to "connect to MySQL with asyncio", "use aiomysql", "set up an async MySQL connection pool", "query MySQL asynchronously in Python", or needs guidance on aiomysql best practices, connection lifecycle, transactions, or cursor types.
Database and HTTP connection pooling patterns for Python async applications. Use when configuring asyncpg pools, aiohttp sessions, or optimizing connection lifecycle in high-concurrency services.
Quick reference mapping global architecture concepts to Python/FastAPI/SQLAlchemy syntax. For concepts, see the global skills.