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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.
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Build Python applications that keep other tasks moving while waiting for network, database, or file operations. Guides the choice of asyncio patterns, with timeouts, cancellation, error handling, and testing for asynchronous code.
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Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.
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Build Python applications that keep other tasks moving while waiting for network, file, or database operations. The skill guides asyncio patterns, task cancellation, timeouts, workload limits, and testing for asynchronous APIs, scrapers, and background processing.
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Scan API route definitions and generate test files for authentication, input validation, error responses, pagination, uploads, and rate limits. Supports Next.js App Router, Express, FastAPI, and Django REST, with Node and Python test outputs.
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Understand a public GitHub repository through GitDiagram's architecture overview. Get an explanation of its main components, source paths, and connections, plus Mermaid diagram source and an interactive diagram for exploring the code.
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Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
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Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
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Builds high-performance async Python APIs with FastAPI and Pydantic V2 including REST endpoints, authentication flows, async SQLAlchemy, and WebSocket endpoints.
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Python FastAPI development with uv package manager, modular project structure, SQLAlchemy ORM, and production-ready patterns.
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Patterns and best practices for integrating ROS2 systems with web technologies including REST APIs, WebSocket bridges, and browser-based robot interfaces. Use this skill when building web dashboards for robots, streaming camera feeds to browsers, exposing ROS2 services as REST endpoints, or implementing bidirectional WebSocket communication between web UIs and ROS2 nodes. Trigger whenever the user mentions rosbridge, rosbridge_suite, roslibjs, FastAPI with ROS2, Flask with rclpy, WebSocket for robot telemetry, MJPEG streaming, WebRTC for robots, REST API wrapping ROS2 services, web-based robot control, browser robot interface, robot dashboard, CORS configuration for robots, or any web-to-ROS2 bridge pattern. Also trigger for authentication on robot web interfaces, rate limiting sensor streams, video streaming from robot cameras to browsers, or running async web frameworks alongside the ROS2 executor. Covers rosbridge_suite, FastAPI, Flask, WebSocket, and WebRTC approaches.
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Automatically migrate Python web applications between frameworks (Flask → FastAPI, Django → FastAPI). Use when you need to migrate an existing web application to a modern framework while preserving functionality. The skill analyzes the codebase, updates routes, handlers, configuration, dependency injection patterns, and tests. Creates git commits for each migration phase and generates a comprehensive summary of all changes. Supports automatic dependency updates, code transformations, and test adaptations.
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Creates FastAPI endpoints with layered architecture (Router → Service → Repository). Use when creating new API endpoints, CRUD operations, or scaffolding a new domain module in a FastAPI project.
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Testing patterns for FastAPI with pytest-asyncio, httpx AsyncClient, fixtures, and test data factories. Use when writing tests, setting up test infrastructure, or improving coverage in a FastAPI project.
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Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.
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FastAPI guidance — typed endpoints, Pydantic validation, dependency injection, async patterns, and production deployment.
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FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.
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FastAPI patterns for high-performance async APIs
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Also use for async Python patterns, Pydantic schemas for LLM structured outputs, retry strategies for LLM calls, background AI jobs, or any service combining FastAPI and LLMs. If Python + AI backend is in scope — use this skill
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FastAPI service setup: app/main.py, API routers, pydantic-settings, lifespan/app.state services, middleware, health checks, pytest/TestClient, Uvicorn, Gunicorn/uvicorn-worker, Docker/Compose. Use for scaffolding or modernizing Python API services; skip for Flask/Django-only, frontend-only, non-Python APIs, or Supabase-specific work.
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Backend Python Specialist IA — Expert en développement backend Python (FastAPI, Django, SQLAlchemy, Celery, async, type safety)
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Backend Python v2 — FastAPI, SQLAlchemy, async, Pydantic, deployment
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Guide for building and refactoring a FastAPI + Prefect + PostgreSQL/pgvector analytics pipeline that extracts insights from chat data using LLM classification, embeddings, UMAP/HDBSCAN clustering, and narrative generation. Use this skill whenever working on this project's codebase — including adding features, refactoring, debugging, creating new endpoints, modifying pipeline stages, or reviewing architecture decisions. Also trigger when the user asks about project structure, stack decisions, async migration, or best practices for this specific system.
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Search across multiple engines through SearXNG, a service that combines search results. Filter by category, date range, or engine, and get titles, links, and snippets. Public instances require no API key.
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