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Found 22 Skills
structlog - structured logging library for Python with native JSON support, context binding, and processor pipeline. Integrates with FastAPI, Django, and standard logging module. USE WHEN: user mentions "structlog", "python structured logging", "context binding", asks about "JSON logging python", "fastapi logging", "django structured logging" DO NOT USE FOR: Standard Python logging - use `python-logging` instead, Node.js logging - use `pino` or `winston`, Java logging - use `slf4j` or `logback` instead
This skill should be used when the user asks to "use SQLModel", "define SQLModel models", "connect SQLModel with FastAPI", "set up a database with SQLModel", or needs guidance on SQLModel best practices, relationships, or session management.
SQLAlchemy Python SQL toolkit and ORM with powerful query builder, relationship mapping, and database migrations via Alembic
Comprehensive Pydantic data validation skill for customer support tech enablement - covering BaseModel, Field validation, custom validators, FastAPI integration, BaseSettings, serialization, and Pydantic V2 features
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.
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
CQRS (Command Query Responsibility Segregation) patterns for separating read and write models. Use when optimizing read-heavy systems, implementing event sourcing, or building systems with different read/write scaling requirements.
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.
SQLAlchemy 2.0 async patterns with AsyncSession, async_sessionmaker, and FastAPI integration. Use when implementing async database operations, connection pooling, or async ORM queries.
Build conversational AI agents using Pydantic AI + OpenRouter. Use when creating type-safe Python agents with tool calling, validation, and streaming.
Bootstrap Python MCP server projects and workspaces on macOS using uv and FastMCP with consistent defaults. Use when creating a new MCP server from scratch, scaffolding a single uv MCP project, scaffolding a uv workspace with package/service members, initializing pytest+ruff+mypy defaults, creating README.md, initializing git, running initial validation checks, or starting from OpenAPI/FastAPI with MCP mapping guidance.