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Found 201 Skills
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.
Build FastAPI services with JWT auth, structlog, and Prometheus metrics. Use when creating or modifying a Python HTTP server, adding authentication, structured logging, or instrumentation to a FastAPI app.
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.
Shared conventions for Next.js 16 + FastAPI full-stack projects. Architecture, code quality, testing, styling, and commands. Referenced by nextjs-fastapi-implementor and nextjs-fastapi-reviewer.
FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.
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.
FastAPI done right. Async patterns, dependency injection, Pydantic v2 models, middleware, and project structure.
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.
FastAPI Python async framework with Pydantic and automatic OpenAPI. Use for Python APIs.
Trigger when the user wants to create a new FastAPI project, add new features, refactor code, or asks about architectural best practices. This skill enforces 2026 clean architecture with SQLModel, Repository Pattern, full async, and production-ready workflow.
Build FastAPI applications using Clean Architecture principles with proper layer separation (Domain, Infrastructure, API), dependency injection, repository pattern, and comprehensive testing. Use this skill when designing or implementing Python backend services that require maintainability, testability, and scalability.
Use when scaffolding production-ready FastAPI services with uv, SQLAlchemy, Alembic, Postgres, Docker, and CI gates.