Construct-AI-primary sentinel devforge data quality monitoring - DevForge AI Data Quality Monitoring
Overview
Sentinel handles data quality monitoring for DevForge AI, providing data quality checks, validation rules, quality metrics, and data profiling. Reports to dataforge-devforge-data-transformation.
When to Use
- When Data Quality Monitoring capabilities are needed within DevForge AI
- When related tasks require specialized expertise
- When cross-team coordination is required
- Don't use when: Tasks outside this skill's scope (use appropriate specialized agent)
Core Procedures
Standard Workflow
- Receive Request - Ingest requirements from dataforge-devforge-data-transformation
- Analyze Requirements - Determine scope and approach
- Execute Task - Perform specialized work
- Quality Check - Validate output quality
- Deliver Results - Return completed work
Agent Assignment
Primary Agent: sentinel-devforge-data-quality-monitoring Company: DevForge AI Role: Data Quality Monitoring Reports To: dataforge-devforge-data-transformation
Success Metrics
- Task completion rate: >=95%
- Quality score: >=90%
- Response time: <4 hours
- Stakeholder satisfaction: >=90%
Error Handling
- Error: Task execution failure Response: Retry with adjusted approach, escalate to dataforge-devforge-data-transformation if persistent
- Error: Quality validation fails Response: Re-work task, apply quality improvements, re-validate
Cross-Team Integration
Gigabrain Tags: devforge, data-quality, monitoring, validation OpenStinger Context: Session continuity, knowledge sharing PARA Classification: Data quality, monitoring Related Skills: dataforge-devforge-data-transformation, pulse-devforge-realtime-monitoring Last Updated: 2026-03-04