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Found 165 Skills
Comprehensive Pydantic data validation skill for customer support tech enablement - covering BaseModel, Field validation, custom validators, FastAPI integration, BaseSettings, serialization, and Pydantic V2 features
Use when FastAPI validation with Pydantic models. Use when building type-safe APIs with robust request/response validation.
Dual skill for deploying scientific models. FastAPI provides a high-performance, asynchronous web framework for building APIs with automatic documentation. Streamlit enables rapid creation of interactive data applications and dashboards directly from Python scripts. Load when working with web APIs, model serving, REST endpoints, interactive dashboards, data visualization UIs, scientific app deployment, async web frameworks, Pydantic validation, uvicorn, or building production-ready scientific tools.
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.
Use when securing FastAPI API endpoints with JWT Bearer token validation, scope/permission checks, or stateless auth - integrates auth0-fastapi-api for REST APIs receiving access tokens from SPAs, mobile apps, or other clients. Also handles DPoP proof-of-possession token binding. Triggers on: Auth0FastAPI, FastAPI API auth, JWT validation, require_auth, DPoP.
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices. Use for Python projects, APIs, data processing, or automation scripts.
REST API and WebSocket development with FastAPI emphasizing security, performance, and async patterns
Expert guidance for integrating ViewComfy API into web applications using Python and FastAPI
Guides the agent through scaffolding and building FastAPI applications, including project structure, API routes, request/response models, path and query parameters, dependency injection, middleware, error handling, and boilerplate generation. Triggered when the user asks to "scaffold a FastAPI project", "create a FastAPI app", "add an API endpoint", "create a router", "add middleware", "implement dependency injection", "handle errors", "set up CORS", "create background tasks", "implement WebSocket", "structure a FastAPI project", "generate boilerplate", or "add authentication".
System architecture guidance for Python/React full-stack projects. Use during the design phase when making architectural decisions — component boundaries, service layer design, data flow patterns, database schema planning, and technology trade-off analysis. Covers FastAPI layer architecture (Routes/Services/Repositories/Models), React component hierarchy, state management, and cross-cutting concerns (auth, errors, logging). Produces architecture documents and ADRs. Does NOT cover implementation (use python-backend-expert or react-frontend-expert) or API contract design (use api-design-patterns).
Server-specific best practices for FastAPI, Celery, and Pydantic. Extends python-skills with framework-specific patterns.
Review FastAPI security audit patterns for dependencies and middleware. Use for auditing auth dependencies, CORS configuration, and TrustedHost middleware. Use proactively when reviewing FastAPI apps. Examples: - user: "Audit FastAPI route security" → check for Depends() and Security() usage - user: "Check FastAPI CORS setup" → verify origins when allow_credentials=True - user: "Review FastAPI middleware" → check TrustedHost and HTTPSRedirect config - user: "Secure FastAPI API keys" → move from query params to header schemes - user: "Scan for FastAPI footguns" → check starlette integration and dependency order