# Data Quality Checker

> Validate CSV, JSON, and JSONL data files for quality issues. Detects missing values, duplicates, type inconsistencies, statistical outliers, format violations, whitespace problems, empty columns, and schema drift. Generates quality score (0-100) with severity-ranked issues. Supports schema validation and auto-schema generation. Use when asked to check data quality, validate CSV/JSON files, find data issues, detect duplicates, check for missing values, validate data types, find outliers, generate data quality reports, or validate against a schema. Triggers on "data quality", "validate CSV", "check data", "data issues", "duplicates", "missing values", "outliers", "data validation", "schema validation", "data profiling".

- Skill: `knownasnaffy/data-quality-checker-4` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add knownasnaffy/data-quality-checker-4`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knownasnaffy/data-quality-checker-4/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: knownasnaffy (https://skillmd.com/u/knownasnaffy)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/knownasnaffy/data-quality-checker-4

---


# Data Quality Checker

Validate CSV/JSON/JSONL data for quality issues. Pure Python, zero dependencies.

## Quick Start

```bash
# Full quality check
python3 scripts/check_data_quality.py data.csv

# JSON/JSONL support
python3 scripts/check_data_quality.py data.json
python3 scripts/check_data_quality.py data.jsonl

# Markdown report
python3 scripts/check_data_quality.py data.csv --format markdown

# JSON report (for CI/CD)
python3 scripts/check_data_quality.py data.csv --format json

# Only specific checks
python3 scripts/check_data_quality.py data.csv --checks missing,duplicates,types

# Only warnings and critical
python3 scripts/check_data_quality.py data.csv --severity warning

# Save report
python3 scripts/check_data_quality.py data.csv --format markdown --output report.md
```

## Schema Validation

```bash
# Generate schema from existing data
python3 scripts/check_data_quality.py data.csv --generate-schema schema.json

# Validate against schema
python3 scripts/check_data_quality.py data.csv --schema schema.json
```

## Checks Performed

| Check | Description | Severity |
|-------|-------------|----------|
| `missing` | Missing/null/empty values per column | info → critical |
| `duplicates` | Duplicate rows and potential ID conflicts | warning |
| `types` | Mixed data types within columns | info → warning |
| `outliers` | Statistical outliers via IQR method | info → warning |
| `formats` | Email/phone/URL/date format violations | warning |
| `whitespace` | Leading/trailing whitespace | info |
| `empty` | Entirely empty columns | warning |
| `drift` | Extra/missing keys across rows (schema drift) | warning |

## Quality Score

0-100 score based on weighted severity:
- **90-100**: Clean data, minor issues
- **70-89**: Usable but needs attention
- **50-69**: Significant issues
- **0-49**: Critical problems

## Exit Codes

- `0` — No warnings or critical issues
- `1` — Warnings found
- `2` — Critical issues found

Use in CI: `python3 scripts/check_data_quality.py data.csv || echo "Quality check failed"`

## Schema Format

JSON schema with validation rules:

```json
{
  "required": ["id", "email", "name"],
  "properties": {
    "id": {"type": "integer", "minimum": 1},
    "email": {"type": "string", "pattern": "^[^@]+@[^@]+\\.[^@]+$"},
    "age": {"type": "number", "minimum": 0, "maximum": 150},
    "status": {"type": "string", "enum": ["active", "inactive", "pending"]}
  }
}
```