# Data Quality Checker

> 1. Skill Name

- Skill: `anup4khandelwal/data-quality-checker` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add anup4khandelwal/data-quality-checker`
- Raw SKILL.md: https://api.skillmd.com/api/skills/anup4khandelwal/data-quality-checker/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: anup4khandelwal (https://skillmd.com/u/anup4khandelwal)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/anup4khandelwal/data-quality-checker

---

## 1. Skill Name
data-quality-checker

## 2. Description
Data Quality Checker: Designs trusted data pipelines, schemas, and quality checks.

## 3. When the AI agent should use this skill
Use this skill when a request aligns with Data Quality Checker in the data domain.

## 4. Required Inputs
- Repository path and target scope
- Objective and acceptance criteria
- Stack details (Node.js, TypeScript, Python, infra, or data)
- Constraints (time, risk, compliance, cost)

## 5. Expected Outputs
- Implementation plan
- Generated or updated artifacts
- Validation steps and results
- Risks and follow-up actions

## 6. Step-by-Step Workflow
1. Clarify scope, assumptions, and success criteria.
2. Inspect repository context and existing patterns.
3. Propose minimal safe change set.
4. Implement artifacts and configuration updates.
5. Run checks/tests and capture evidence.
6. Summarize outcomes, limitations, and next steps.

## 7. Commands or tools used
- sqlfluff, dbt, airflow/prefect, duckdb/psql
- rg --files
- rg "pattern"
- git status
- git diff

## 8. Example prompts
- /skill data-quality-checker Audit current implementation and propose prioritized improvements.
- /skill data-quality-checker Implement a production-ready baseline with validation steps.
- /skill data-quality-checker Generate artifacts and summarize tradeoffs.

## 9. Guardrails
- Do not run destructive actions without explicit approval.
- Preserve existing conventions unless migration is requested.
- Prefer deterministic and reproducible commands.
- Clearly state assumptions and uncertainty.

## 10. Limitations
- Output quality depends on project context and available tests.
- External systems may require environment-specific verification.
- Human review may still be needed for policy/compliance decisions.