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Found 176 Skills
Comprehensive pytest testing guide for FastAPI backends. Covers unit testing, integration testing, async patterns, mocking, fixtures, coverage, and FastAPI-specific testing with TestClient. Use when writing or updating test code for backend services, repositories, or API routes.
FastAPI OpenTelemetry style: native FastAPIInstrumentor, centralized observability init, Python decorators, OTLP logs, and LLM cost metrics.
Python backend testing patterns with pytest for FastAPI applications. Use when writing Python tests: unit tests for services and repositories, integration tests for API endpoints with httpx.AsyncClient, fixture creation, factory setup with factory_boy, async testing with pytest-asyncio, mocking strategies, and parametrized tests. Covers test organization (tests/unit, tests/integration), conftest hierarchy, and coverage requirements. Does NOT cover frontend tests (use react-testing-patterns) or E2E browser tests (use e2e-testing).
Guides the agent through implementing authentication and authorization in FastAPI applications. Triggered when users ask to "add authentication", "implement login", "add JWT tokens", "create OAuth2 flow", "hash passwords", "protect endpoints", "add role-based access", "implement RBAC", "add API key auth", "secure the API", or mention authentication, authorization, JWT, OAuth2, password hashing, bcrypt, access tokens, refresh tokens, security dependencies, or API security.
Guides the agent through running and configuring ASGI servers (Uvicorn, Granian, Hypercorn) for Python web applications. Triggered when users say "run a FastAPI app", "configure uvicorn", "set up ASGI server", "deploy with uvicorn", "configure workers", "set up SSL/TLS", "run development server", "configure hot reload", or mention ASGI server, production deployment, server configuration, uvicorn, granian, or hypercorn.
Strawberry GraphQL library for Python with FastAPI integration, type-safe resolvers, DataLoader patterns, and subscriptions. Use when building GraphQL APIs with Python, implementing real-time features, or creating federated schemas.
Automatically generate comprehensive backend API documentation in AGENTS.md format. Use when the user requests to: (1) Document backend API endpoints, (2) Update backend API specifications after code changes, (3) Create or refresh backend/AGENTS.md with complete API documentation including request/response schemas, business rules, and authentication details, (4) Generate API documentation from FastAPI route files
Generate comprehensive AGENTS.md documentation for backend projects with complete API specifications, business rules, data models, and data flows. Use when (1) Creating AGENTS.md from existing CLAUDE.md, (2) Documenting backend API modules with FastAPI routes, (3) Migrating documentation to AGENTS.md/CLAUDE.md symlink structure, (4) Adding complete API interface documentation to existing specs, (5) Creating module-level AGENTS.md for specific features (mcp, teamo_code, file_system, etc.)
Plan and execute upgrades for Python libraries, handling breaking changes. Use when performing major version bumps for frameworks like Django or FastAPI.
Master FastAPI dependency injection for building modular, testable APIs. Use when creating reusable dependencies and services.
Create Galaxy REST API endpoints with FastAPI routers, Pydantic schemas, and manager pattern. Use for: new API routes, FastAPI endpoints, REST resources, Pydantic request/response models, lib/galaxy/webapps/galaxy/api routers, lib/galaxy/schema definitions, API controller creation.
Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.