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Found 107 Skills
Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.
Build Python APIs with FastAPI, Pydantic v2, and SQLAlchemy 2.0 async. Covers project structure, JWT auth, validation, and database integration with uv package manager. Prevents 7 documented errors. Use when: creating Python APIs, implementing JWT auth, or troubleshooting 422 validation, CORS, async blocking, form data, background tasks, or OpenAPI schema errors.
FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.
Backend development agent for Resume Matcher. Handles FastAPI endpoints, Pydantic schemas, TinyDB operations, LiteLLM integration, and Python service logic. Use when creating or modifying backend code.
Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.
Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke for async SQLAlchemy, JWT authentication, WebSockets, OpenAPI documentation.
Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or implementing environment-specific behavior.
Generar modelos Pydantic a partir de OpenAPI/JSON Schema como fuente única de verdad
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
Use when FastAPI validation with Pydantic models. Use when building type-safe APIs with robust request/response validation.
FastAPI production-grade best practices and guidelines for building scalable, high-performance web APIs. Covers project structure, async concurrency, Pydantic validation, dependency injection, and database patterns.
Comprehensive data validation using Pydantic v2 with data quality monitoring and schema alignment for PlanetScale PostgreSQL. Use when implementing API validation, database schema alignment, or data quality assurance. Triggers: 'validation', 'Pydantic', 'schema', 'data quality'.