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custom-criteria-audit

Audit semantic views against user-defined natural-language validation criteria.

Updated Mar 7, 2026
Search Tags:#automation#validation#audit#nlp#data-quality#semantic-view#custom-criteria
One-click install
npx skills add https://github.com/randoneering/nix-flake-mirror --skill custom-criteria-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: custom-criteria-audit
Source: https://github.com/randoneering/nix-flake-mirror/tree/main/home/programs/opencode/skills/snowflake/semantic-view-optimization/audit/custom_criteria
Command: npx skills add https://github.com/randoneering/nix-flake-mirror --skill custom-criteria-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This tool enables teams to define and evaluate validator rules against their semantic view using user-specified, natural-language criteria, reducing manual QA effort and ensuring model quality.

Core Features & Use Cases

  • Define and apply custom validation criteria against semantic view components (measures, dimensions, tables, and relationships).
  • Automatically parse user-provided criteria into actionable validation steps and create a validation plan.
  • Generate structured audit results with clear compliance status and recommended fixes for governance and data quality.

Quick Start

Provide your custom criteria when prompted to begin the audit of your semantic view.

Frequently Asked Questions about custom-criteria-audit

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

FAQPage Schema
How do I validate a semantic view using custom natural language criteria?▼

You can validate a semantic view by providing custom natural language criteria, which the audit tool parses into a validation plan. It assesses measures, dimensions, tables, and relationships to check data-model quality and compliance.

What does a semantic view audit check for in data model quality?▼

A semantic view audit checks data model quality by applying user-defined validation rules across measures, dimensions, tables, and relationships. It produces structured results with compliance metrics and recommended remediation steps.

Can I automate data quality validation rules for my semantic model?▼

Yes, you can automate data quality validation by defining custom rules in natural language. The tool automatically parses these criteria into actionable validation steps and generates structured audit results with compliance status.

How do I get remediation recommendations for semantic view compliance issues?▼

Remediation recommendations are generated by auditing your semantic view against custom criteria. The audit produces structured results that include clear compliance status and recommended fixes for data quality governance.

What is the best way to audit measures and dimensions against specific rules?▼

The best way to audit measures and dimensions is to define your specific validation criteria in natural language. The tool applies these rules across your semantic view components and enforces mandatory TODO creation for any required remediation.

Do I need pre-defined validation scripts to audit my semantic view?▼

No, you do not need pre-defined validation scripts. You simply provide your custom criteria in natural language when prompted, and the tool automatically parses them into an actionable validation plan to assess your data model.

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