1.2.0
Data quality validation patterns for daily checks and anomaly follow-up.
76 downloads
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Data Quality Operations
Use when dataset freshness/completeness checks must be run consistently.
Inputs to Gather
- Primary target (service, team, or dataset)
- Current impact and urgency
- Assigned owner and deadline
Core Commands
dq profile --dataset <name>dq validate --rule-set <id>dq anomaly --open --metric <name>workflow checklist --from templates/checklist.mdworkflow report --from templates/report.md
Operating Notes
- Prefer explicit owner assignment before action.
- Keep timeline notes concise and timestamped.
- Save output artifacts for audit and handoff.
- This version adds a structured report template for post-task summaries.
Version marker: data-quality-operations 1.2.0
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ZIP package — ready to use
Skill Info
- Creator
- JiaranI
- Downloads
- 76
- Published
- Mar 15, 2026
- Updated
- Mar 16, 2026