Read a PostgreSQL schema and output a SQLAlchemy 2.0 declarative models file with mapped columns, foreign keys, constraints, defaults, and enum classes.
Create a new database connection in Apache Superset by providing connection details like SQLAlchemy URI, engine type, and database name to enable data visualization.
Scan a Python project for patterns that break obfuscation (eval, dynamic attribute access, reflection) and receive severity counts, detected frameworks, and a suggested preset.
Test a database connection by validating a SQLAlchemy URI without creating it. Ensure the URI is reachable from the Superset server to prevent setup issues.
Extract and document database models from a code repository, listing tables, fields, types, and relationships across SQLAlchemy, Django ORM, Prisma, TypeORM, and Mongoose.
Create a new project with any supported framework such as Spring Boot, React, Vue, Next.js, FastAPI, and others. Configure options including TypeScript, testing, Docker, and database.
Retrieve stack health diagnostics, graph statistics, and verification metrics for a repository to identify issues across React, FastAPI, and SQLAlchemy layers.
Create Pydantic models and SQLAlchemy ORM models with relationships by providing entity names and relation formats, saving time on FastAPI model scaffolding.
Identify tables frequently used together in database queries to infer relationships between database objects. Returns fully rendered SQL with metadata.