d6e/data-quality-checker

Data Quality Checker

Validate any dataset against configurable rules. Identifies missing fields, type mismatches, format violations, and out-of-range values.

What's Included

• Template Prompt: Data quality analyst persona
• STF: validate-records — Configurable rule engine supporting 6 check types

Supported Checks

Check	Description	Example
required	Field must be present and non-empty	email must exist
type	Value must match expected type	age must be number
pattern	Value must match regex	email must match ^.+@.+$
min	Numeric minimum	price >= 0
max	Numeric maximum	quantity <= 10000
enum	Value must be in allowed list	status in ["active", "inactive"]

Use Cases

• Validate CSV/JSON imports before processing
• Audit existing datasets for quality issues
• Generate data quality reports with pass rates
• Define reusable validation rule sets for recurring data pipelines

Example

Provide your data and say: "Check these customer records — email should be required, age should be a number between 0 and 150, and status should be active or inactive."