data-quality-validator

Name: data-quality-validator
Availability: InStock
Author: Pupfish

Identify data quality issues in loaded Qlik datasets and generate a validation report.

1|Updated Mar 13, 2026

Search Tags:#validation #mcp #report #data-quality #data-model #qa-review #qlik

Ready to run

npx skills add https://github.com/Pupfish-LLC/qlik-agents --skill data-quality-validator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality-validator
Source: https://github.com/Pupfish-LLC/qlik-agents/tree/main/skills/data-quality-validator
Command: npx skills add https://github.com/Pupfish-LLC/qlik-agents --skill data-quality-validator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Post-load data quality validation catches issues that successful reloads can mask, including null rates, referential integrity gaps, unusual value distributions, row count mismatches, and duplicates. It also provides templates and patterns for embedding checks in load scripts and for post-load validation to produce a consistent Data Quality Validation Report.

Core Features & Use Cases

Null Rate Analysis
Referential Integrity
Value Distribution
Row Count Validation
Duplicate Detection
Sparse Field Analysis
Field Type Consistency
Post-Load Validation Queries and MCP support

Quick Start

Run the data-quality-validator after a load to generate a Data Quality Validation Report and surface issues across null rates, referential integrity, and row counts.

Frequently Asked Questions about data-quality-validator

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

FAQPage Schema

How do I check data quality in Qlik after a data load?▼

Post-load data quality validation inspects loaded Qlik datasets to identify null rates, referential integrity gaps, duplicates, and row count mismatches. It generates a standardized Data Quality Validation Report using reusable Qlik resident queries and MCP-SQL templates.

What is referential integrity validation in Qlik datasets?▼

Referential integrity validation checks relationships between loaded Qlik datasets to identify orphaned records and key mismatches. The validator generates queries and includes findings in a standardized Data Quality Validation Report.

Can I detect null rates and sparse fields in Qlik load scripts?▼

Yes, null rate and sparse field analysis can be performed using provided Qlik resident queries and MCP-SQL templates. These templates support embedding checks directly in load scripts to document field type consistency and value distribution.

Does this data quality validator work with MCP-SQL templates?▼

Yes, the validator provides reusable MCP-SQL templates to validate data and document findings. These templates support post-load inspection across null rates, duplicates, row count validation, and field type consistency.

What is the best way to validate row counts and duplicates in Qlik apps?▼

The best way to validate row counts and duplicates is running post-load inspection using standardized Qlik resident queries. This produces a Data Quality Validation Report surfacing row count mismatches and duplicate records.