pg-data

To generate SQL from natural language for data analytics, you submit a plain-text data question to the skill, which translates it into a safe, read-only SELECT query with appropriate joins and filters against your target database.

The safest way to run natural-language SQL queries on production data is using a tool that enforces strict read-only constraints and generates parameterized SELECT queries to prevent unauthorized data modifications or injection attacks.

Yes, you can use natural-language SQL generation for multi-tenant database isolation because the skill enforces security constraints and respects multi-tenant boundaries when translating user questions into queries.

You query an ecommerce database schema using plain English by describing your data request, which triggers the skill to read the target database schema and generate a parameterized SQL query with sensible limits.

Yes, natural-language to SQL translation supports multiple database schemas, specifically providing cross-database support for blog_small, ecommerce_medium, and saas_crm_large with schema-aware query generation.

Limitations of natural-language SQL generation include strict read-only enforcement, meaning it cannot perform write operations, and it is restricted to querying only the specific database schemas it is configured to access.