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Found 60 Skills
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring AI-generated Python code (deslopification), (6) Designing API patterns, or (7) Optimizing backend performance.
Quick reference mapping global architecture concepts to Python/FastAPI/SQLAlchemy syntax. For concepts, see the global skills.
Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.
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.
Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".
Guides FastAPI backend design using Domain-Driven Design (DDD) and Onion Architecture in Python. Use when structuring a FastAPI app (routes/handlers, Pydantic schemas, Depends-based DI), modeling domain Entities/Value Objects, defining repository interfaces, implementing SQLAlchemy infrastructure adapters, or writing use cases, based on the dddpy reference.
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.
Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-driven planning to clarify data models, authentication method, pagination strategy, and caching before writing any code.
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.
This skill should be used when the user asks to "set up Alembic migrations", "create a database migration", "run alembic upgrade", "configure alembic autogenerate", or needs guidance on SQLAlchemy schema versioning and migration best practices.
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).
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.