{"categories":[{"label":"Quality Assurance","url":"https://skillfed.io/packages/category/software-development-quality-assurance/2"}],"enrichment":{"capability":"Adds context-aware validation functions to FabricDataFrames that automatically appear in autocomplete based on your data's types and metadata, enabling email, credit card, and other domain-specific validators to work seamlessly on Power BI data.","skillfed_tags":["power-bi","data-validation","semantic-functions"],"use_cases":["Validate email addresses in customer contact columns with automatic discovery when both email and string types are present.","Check credit card numbers and international payment identifiers (IBAN, NIE) in financial data columns.","Build data quality checks in Power BI workflows where validation functions adapt to your schema.","Prepare and clean datasets by having relevant validators surface contextually in autocomplete.","Integrate custom domain-specific validators into FabricDataFrame workflows using the @semantic_function decorator."],"what_it_does":"This package extends FabricDataFrames with semantic validation functions that intelligently surface only relevant validators based on your data's structure and metadata. Rather than manually selecting validators, the package examines column data types, Power BI data categories, and actual data content to determine which validators make sense\u2014so an email validator appears in autocomplete only when you have both a string column and email-like data. The validators themselves handle common validation tasks like email addresses, credit card numbers, and international identifiers (IBAN, NIE).\n\nIt works by decorating validation functions with @semantic_function, which registers them for automatic discovery. The package integrates with semantic-link-sempy and validators, providing a bridge between Power BI's data context and Python validation logic. This is particularly useful in data preparation workflows where you want validation suggestions to adapt to the shape of your data rather than requiring explicit function calls.","worth_installing":"Yes, if you work with FabricDataFrames and Power BI data. The package is actively maintained, has no security vulnerabilities, and low install friction. The permissive MIT license removes legal concerns. Install it when you need context-aware validation that adapts to your data structure rather than requiring explicit validator selection."},"id":"semantic-link-functions-validators","links":{"html":"https://skillfed.io/packages/semantic-link-functions-validators","md":"https://skillfed.io/packages/semantic-link-functions-validators.md","pypi":"https://pypi.org/project/semantic-link-functions-validators/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-21","license_spdx":null,"license_treatment":"permissive","name":"semantic-link-functions-validators","python_support":"supports_current","summary":"Semantic link functions for validators package. Enables validation of email addresses, credit card numbers, ... in FabricDataFrames."},"popularity":{"monthly_downloads":473070,"position":6470,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.14.2"}