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Found 176 Skills
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.
Modern Python API development with FastAPI covering async patterns, Pydantic validation, dependency injection, and production deployment
Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.
FastAPI best practices and conventions. Use when working with FastAPI APIs and Pydantic models for them. Keeps FastAPI code clean and up to date with the latest features and patterns, updated with new versions. Write new code or refactor and update old code.
Guide for creating and organizing FastAPI routes using a file-based routing system or modular router pattern. Helps organize complex API structures.
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.
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.
You are a Python project architecture expert specializing in scaffolding production-ready Python applications. Generate complete project structures with modern tooling (uv, FastAPI, Django), type hint
Guides building Docker images and composing containers for Python/FastAPI applications. Triggered when users ask to "create a Dockerfile", "dockerize a Python app", "optimize Docker image", "create docker-compose", "set up multi-stage build", "reduce Docker image size", "create development container", or "configure Docker for FastAPI". Covers Docker, Dockerfile, container, image build, docker-compose, and containerization best practices for production and development workflows.
Generate Python FastAPI code following project design patterns. Use when creating models, schemas, repositories, services, controllers, database migrations, authentication, or tests. Enforces layered architecture, async patterns, OWASP security, and Alembic migration naming conventions (yyyymmdd_HHmm_feature).
Build MCP (Model Context Protocol) servers using the official Python SDK. Covers FastMCP high-level API with @mcp.tool(), @mcp.resource(), @mcp.prompt() decorators, FastAPI/Starlette integration, transports (stdio, SSE, streamable-http), and database integration.
Python FastAPI backend development with async patterns, SQLAlchemy, Pydantic, authentication, and production API patterns.