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data-quality

Configure quality rules and generate reports across knowledge networks.

Updated Aug 23, 2026
Search Tags:#data-quality#batch-processing#data-inspection#rule-management#data-view#knowledge-network#quality-rule
One-click install
npx skills add https://github.com/kweaver-ai/kweaver-dip --skill data-quality-kweaver-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-quality
Source: https://github.com/kweaver-ai/kweaver-dip/tree/main/skills/data-quality
Command: npx skills add https://github.com/kweaver-ai/kweaver-dip --skill data-quality-kweaver-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data quality management across data sources, views, and knowledge networks is centralized, automated, and auditable, enabling governance and reliable reporting.

Core Features & Use Cases

  • Quality rule management: create, query, update, and delete rules across views and data sources.
  • Logic view and data exploration: fetch view metadata and field details to drive rule configuration.
  • Knowledge-network integration: apply quality checks within knowledge networks and object types.
  • Batch processing: scale rule creation across many views with controlled processing.

Quick Start

Load a knowledge-network view, configure baseline rules, and start a quality inspection workflow.

Frequently Asked Questions about data-quality

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

FAQPage Schema
How do I automate data quality checks across a knowledge network?▼

Automate data quality checks by configuring quality rules, querying logical views, and generating reports across knowledge networks. This solution centralizes governance and enables reliable, auditable reporting across multiple data sources.

Can I apply data quality rules to multiple views at once?▼

Yes, you can apply data quality rules to multiple views at once using batch processing. This scales rule creation across many data sources and views with controlled processing and built-in error handling.

How do I configure quality rules using SQL expressions?▼

Configure quality rules by writing SQL-99 compliant expressions using technical field names. The system enforces non-empty rule configurations and requires unique rule names for each logical view to ensure consistent score presentation.

Does data quality governance work with logical views and object types?▼

Yes, data quality governance works with logical views and object types within knowledge networks. You can fetch view metadata and field details to drive rule configuration and apply quality checks directly.

What are the limitations of rule configuration for data quality management?▼

Rule configuration for data quality management requires non-empty rule_config parameters, SQL-99 compliant expressions using technical names, and unique rule names per view. It does not support inconsistent score presentation or duplicate naming.

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