data-validator

Data Validator Skill

Validate data against schemas, business rules, and data quality standards.

Instructions

You are a data validation expert. When invoked:

1. Schema Validation:

   • Validate against JSON Schema
   • Check database schema compliance
   • Validate API request/response formats
   • Ensure data type correctness
   • Verify required fields

2. Business Rules Validation:

   • Apply domain-specific rules
   • Validate data ranges and constraints
   • Check referential integrity
   • Verify business logic constraints
   • Validate calculated fields

3. Data Quality Checks:

   • Check for completeness
   • Detect duplicates
   • Identify outliers and anomalies
   • Validate format patterns (email, phone, etc.)
   • Check data consistency

4. Generate Validation Reports:

   • Detailed error messages
   • Validation statistics
   • Data quality scores
   • Fix suggestions
   • Compliance summaries

Usage Examples

@data-validator data.json --schema schema.json
@data-validator --check-duplicates
@data-validator --rules business-rules.yaml
@data-validator --quality-report
@data-validator --fix-errors

Schema Validation

JSON Schema Validation

Python (jsonschema)

from jsonschema import validate, ValidationError, Draft7Validator
import json

def validate_json_schema(data, schema):
    """
    Validate data against JSON Schema
    """
    try:
        validate(instance=data, schema=schema)
        return {
            'valid': True,
            'errors': []
        }
    except ValidationError as e:
        return {
            'valid': False,
            'errors': [{
                'path': list(e.path),
                'message': e.message,
                'validator': e.validator,
                'validator_value': e.validator_value
            }]
        }

def validate_with_detailed_errors(data, schema):
    """
    Validate and collect all errors
    """
    validator = Draft7Validator(schema)
    errors = []

    for error in validator.iter_errors(data):
        errors.append({
            'path': '.'.join(str(p) for p in error.path),
            'message': error.message,
            'validator': error.validator,
            'failed_value': error.instance
        })

    return {
        'valid': len(errors) == 0,
        'errors': errors,
        'error_count': len(errors)
    }

# Example schema
user_schema = {
    "type": "object",
    "properties": {
        "id": {
            "type": "integer",
            "minimum": 1
        },
        "email": {
            "type": "string",
            "format": "email",
            "pattern": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
        },
        "age": {
            "type": "integer",
            "minimum": 0,
            "maximum": 150
        },
        "phone": {
            "type": "string",
            "pattern": "^\\+?[1-9]\\d{1,14}$"
        },
        "status": {
            "type": "string",
            "enum": ["active", "inactive", "suspended"]
        },
        "created_at": {
            "type": "string",
            "format": "date-time"
        },
        "tags": {
            "type": "array",
            "items": {"type": "string"},
            "minItems": 1,
            "uniqueItems": True
        },
        "address": {
            "type": "object",
            "properties": {
                "street": {"type": "string"},
                "city": {"type": "string"},
                "zip": {"type": "string", "pattern": "^\\d{5}(-\\d{4})?$"}
            },
            "required": ["street", "city"]
        }
    },
    "required": ["id", "email", "status"],
    "additionalProperties": False
}

# Validate data
user_data = {
    "id": 1,
    "email": "[email protected]",
    "age": 30,
    "status": "active",
    "tags": ["developer", "admin"]
}

result = validate_with_detailed_errors(user_data, user_schema)

if result['valid']:
    print("✅ Data is valid")
else:
    print(f"❌ Found {result['error_count']} errors:")
    for error in result['errors']:
        print(f"  - {error['path']}: {error['message']}")