# Data Quality Operations

> Data quality validation patterns for daily checks and anomaly follow-up.

- Skill: `dvcrn/data-quality-operations` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add dvcrn/data-quality-operations`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/data-quality-operations/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/data-quality-operations

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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.md`
- `workflow 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