table-validation

What problem does it solve?

When data pipelines create new tables, mismatches between the actual table schema (column names, data types, nullability rules) and the expected contract can cause downstream analytics failures, broken reports, and data quality issues for business users. This Skill automatically catches these schema mismatches immediately after table creation, before the table is used by other processes.

Core Features & Use Cases

• Column Set Validation: Confirms all expected columns are present, and flags any unexpected columns when strict matching is required.
• Type Matching: Verifies each column's declared data type aligns with the expected contract, allowing widening only when explicitly permitted.
• Nullability Checks: Ensures columns marked as NOT NULL in the contract do not allow null values in the actual table.
• Use Case: After running a CTAS pipeline to build a monthly sales fact table, use this Skill to confirm the table has exactly the expected 12 columns, that the order_id column is an integer NOT NULL, and that the sale_date column is a DATE type, catching any accidental schema changes from the source query.

Quick Start

Run the table-validation skill immediately after any table creation or write operation to confirm the table's column set, data types, and nullability match the expected contract.