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table-validation

Validate database table column sets, data types, and nullability against expected contracts.

1.5k|222|Updated Jul 4, 2025
Search Tags:#data engineering#etl#schema validation#table validation#column contract#nullability check#type matching
One-click install
npx skills add https://github.com/Datus-ai/Datus-agent --skill table-validation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: table-validation
Source: https://github.com/Datus-ai/Datus-agent/tree/main/datus/resources/skills/table-validation
Command: npx skills add https://github.com/Datus-ai/Datus-agent --skill table-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about table-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate table schema after CTAS pipeline execution?▼

To validate table schema after CTAS pipeline execution, run this Skill immediately after table creation to verify that column sets, declared data types, and nullability settings match your predefined contract.

What is schema drift in ETL pipelines and how do I prevent it?▼

Schema drift in ETL pipelines occurs when table column names, data types, or nullability rules change unexpectedly; this Skill prevents it by enforcing blocking column contract validation immediately after table creation.

How do I check if database columns match expected nullability and data types?▼

To check if database columns match expected nullability and data types, apply this Skill's type matching and nullability checks to ensure NOT NULL columns are enforced and data types align with the contract.

Does this schema validation tool check row counts or object existence?▼

No, this schema validation tool explicitly excludes object existence checks, row count verification, and data-content assertions, focusing narrowly on column set, data type, and nullability validation handled by separate layers.

When should I use column contract validation in data engineering workflows?▼

Use column contract validation in data engineering workflows involving CTAS table creation, layered ETL pipelines, and schema bootstrapping to prevent schema drift from breaking downstream data consumers.

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