data-quality-checker

name	data-quality-checker
description	Design data quality monitoring with validation rules and alerts. TRIGGERS - Use when user needs help with data-quality-checker related tasks.
category	AI & Automation
version	1

data-quality-checker

Workflow

1. Understand Requirements - Context, audience, goals, constraints
2. Research & Analyze - Best practices, frameworks, background
3. Create Draft - Structured, actionable, tailored output
4. Review & Refine - Quality check, consistency, completeness

Output Format

Context: [Situation] | Objective: [Goal] | Audience: [Who]

Key Components

Component	Details	Owner	Timeline
[Item]	[Specifics]	[Who]	[When]

Action Plan

1. Immediate: [First steps]
2. Short-term: [Build momentum]
3. Medium-term: [Scale]

Success Metrics

Metric	Target	Method
[KPI]	[Goal]	[Track]

Quality Checklist

• Tailored to context
• Actionable steps
• Measurable criteria
• Professional format