outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
instructor
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
python-fastapi-ai
Python FastAPI patterns, Alembic migrations, Pydantic v2, and AI Engineering (raw patterns, LangChain, Google ADK). This skill should be used when working with Python APIs, database migrations, or AI/LLM applications.
python-fastapi-patterns
FastAPI web framework patterns. Triggers on: fastapi, api endpoint, dependency injection, pydantic model, openapi, swagger, starlette, async api, rest api, uvicorn.
data-validation
Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing form validation, API input validation, JSON Schema, Zod, Pydantic, sanitization, XSS prevention, or custom validators.
sqlalchemy-postgres
Expert guidance for SQLAlchemy 2.0 + Pydantic + PostgreSQL. Use when setting up database layers, defining models, creating migrations, or any database-related work. Automatically activated for DB tasks.
