validation

Name: validation
Availability: InStock
Author: Honeydew

Validate Honeydew semantic model objects for integrity and correctness.

39|1|Updated Feb 23, 2026

Search Tags:#validation #data integrity #data quality #metrics #attributes #semantic modeling #honeydew

Ready to run

npx skills add https://github.com/honeydew-ai/honeydew-ai-coding-agents-plugins --skill validation-honeydew-ai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: validation
Source: https://github.com/honeydew-ai/honeydew-ai-coding-agents-plugins/tree/main/plugins/semantic-modeling-tools/skills/validation
Command: npx skills add https://github.com/honeydew-ai/honeydew-ai-coding-agents-plugins --skill validation-honeydew-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures that any data object (metric, attribute, entity, domain) created or modified within the Honeydew semantic layer is valid, functional, and returns sensible results, preventing errors and data inconsistencies.

Core Features & Use Cases

Type-Specific Checks: Performs tailored validation for metrics, attributes, entities, and domains.
Error Detection: Identifies compilation errors, unexpected NULLs, incorrect magnitudes, and logical inconsistencies.
Cross-Validation: Compares related objects to ensure consistency (e.g., filtered metrics against totals).
Use Case: After creating a new sales metric, this skill will automatically run it, check if the revenue is positive and within a reasonable range, and compare it against the total revenue metric to ensure it's a valid subset.

Quick Start

Validate the newly created 'customer_lifetime_value' metric.

Frequently Asked Questions about validation

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

FAQPage Schema

How do I validate metrics and attributes in a semantic model?▼

Data validation for semantic models involves executing generated metrics, attributes, entities, and domains to perform sanity checks on returned data. It identifies compilation errors, unexpected NULLs, and incorrect magnitudes to ensure data integrity.

What is cross-validation for metrics and how does it work?▼

Cross-validation compares related semantic model objects to ensure consistency, such as checking filtered metrics against total metrics. This verifies that a subset metric returns values that are logically consistent with the overall total.

How do I check data integrity for semantic model domains?▼

You check data integrity by running type-specific validation checks for domains, entities, metrics, and attributes. The process executes these objects to detect logical inconsistencies and verify that returned data falls within expected ranges.

Why does my semantic model metric return unexpected NULLs?▼

Unexpected NULLs in semantic model metrics are identified during execution and sanity checks. The validation process detects these NULLs alongside compilation errors and incorrect magnitudes, alerting you to potential data inconsistencies or logical issues.

Can I automatically check if a newly created metric returns sensible results?▼

Yes, you can validate newly created metrics by automatically running them to check if values like revenue are positive and within a reasonable range. It also compares the metric against related totals to ensure it is a valid subset.