How does it handle multi-entity joins?

It performs cross-entity query tests and monitors row counts to ensure that joins are behaving as expected without unintended data duplication (fan-out).

Can it validate domain filters?

Yes, it compares scoped and unscoped queries to verify that domain filters are correctly restricting data as defined in the semantic model.

What does the Validation skill actually do?

It runs automated tests on Honeydew objects like metrics and entities to ensure they return data, follow business logic, and don't contain errors like negative counts or broken joins.

Does it automatically fix SQL errors?

No, it identifies findings and alerts the user with specific details and suggested next steps, allowing the developer to review and confirm any changes.

When should I use this skill?

This skill is designed to be used as a mandatory step every time you create or modify a Honeydew object to prevent regressions and catch data issues early.

Semantic Data Validation

Name: Semantic Data Validation
Author: honeydew-ai

byhoneydew-ai

0•

Database Management

Validates Honeydew semantic objects to ensure data integrity, logical consistency, and correct execution of metrics and entities.

The Validation skill provides a mandatory framework for ensuring that Honeydew objects—including metrics, attributes, entities, and domains—function correctly after creation or modification. It automates complex sanity checks such as verifying non-null results, checking business logic (e.g., ensuring revenue isn't negative), and confirming that joins don't cause data fan-out. By comparing pre- and post-update states and executing scoped queries, it helps developers identify data anomalies and schema errors early in the development lifecycle.

Key Features

01Intelligent error handling that distinguishes between API issues and data anomalies

02Type-specific sanity checks for metrics, attributes, and entities

03Pre- and post-modification data comparison to track logic changes

04Domain-scoped query testing to verify filters and field selectors

050 GitHub stars

06Automated join validation to detect row count explosion and fan-out issues

Use Cases

01Ensuring complex multi-entity relations are correctly mapped without creating duplicate rows

02Verifying that a new financial metric returns sensible values and follows business logic rules

03Testing that domain-level security filters and field exclusions are properly applied to scoped queries

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add honeydew-ai/honeydew-ai-coding-agents-plugins validation

For use in Claude.ai and ChatGPT

Key Features

01Intelligent error handling that distinguishes between API issues and data anomalies

02Type-specific sanity checks for metrics, attributes, and entities

03Pre- and post-modification data comparison to track logic changes

04Domain-scoped query testing to verify filters and field selectors

050 GitHub stars

06Automated join validation to detect row count explosion and fan-out issues

Use Cases

01Ensuring complex multi-entity relations are correctly mapped without creating duplicate rows

02Verifying that a new financial metric returns sensible values and follows business logic rules

03Testing that domain-level security filters and field exclusions are properly applied to scoped queries

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add honeydew-ai/honeydew-ai-coding-agents-plugins validation

For use in Claude.ai and ChatGPT