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data-validation

data-validation

github.com/majesticlabs-devData & databases5080 installsUpdated 7w ago
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About data-validation

A data-validation skill for data engineers building validation pipelines with custom business rules. It provides reusable checks and pipeline patterns using pandas, with guidance on when framework-specific alternatives may fit better.

Key features

  1. 01Check duplicates and referential integrity
  2. 02Validate date ranges and allowed values
  3. 03Compose checks into validation pipelines
  4. 04Run config-driven validation
  5. 05Assert schemas, nulls, and uniqueness

Use cases

  1. 01Validate business rules in data pipelines
  2. 02Check records during ETL processing
  3. 03Add data-quality assertions to pytest tests

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill data-validation

After installing, type/data-validationin Claude Code to confirm it loaded.

Bundled filesscripts/validators.py

Details

From the project README

Data Validation

Audience: Data engineers building validation pipelines.

Goal: Provide validation patterns for custom business rules.

Framework-specific skills:

  • pydantic-validation - Record-level validation with Pydantic
  • pandera-validation - DataFrame schema validation
  • great-expectations - Pipeline expectations and monitoring

Scripts

Execute validation functions from scripts/validators.py:

from scripts.validators import (
    ValidationResult,
    DataValidator,
    validate_no_duplicates,
    validate_referential_integrity,
    validate_date_range,
    validate_value_in_set,
    run_validation_pipeline,
    validate_with_schema_version,
    assert_schema_match,
    assert_no_nulls,
    assert_unique,
    assert_values_in_set
)

Framework Selection

Use CaseFramework
API request/responsePydantic
Record-by-record ETLPydantic
DataFrame validationPandera
Type hints for DataFramesPandera
Pipeline monitoringGreat Expectations
Data warehouse checksGreat Expectations
Custom business rulesCustom functions (this skill)

Usage Examples

Basic Validation
from scripts.validators import validate_no_duplicates, validate_referential_integrity

# Check duplicates
result = validate_no_duplicates(df, cols=['id'])
if not result.passed:
    print(f"Error: {result.message}")
    print(result.failed_rows)

# Check referential integrity
result = validate_referential_integrity(df, 'user_id', users_df, 'id')
Validation Pipeline
from scripts.validators import DataValidator, validate_no_duplicates, validate_date_range

validator = DataValidator()
validator.add_check(lambda df: validate_no_duplicates(df, ['id']))
validator.add_check(lambda df: validate_date_range(df, 'created_at', '2020-01-01', '2025-12-31'))

results = validator.validate(df)
if not results['passed']:
    for check in results['checks']:
        if not check['passed']:
            print(f"Failed: {check['message']}")
Config-Driven Pipeline
from scripts.validators import run_validation_pipeline

config = {
    'unique_columns': ['id'],
    'date_ranges': {
        'created_at': ('2020-01-01', '2025-12-31'),
        'updated_at': ('2020-01-01', '2025-12-31')
    }
}

clean_df, results = run_validation_pipeline(df, config)
Test Assertions
from scripts.validators import assert_schema_match, assert_no_nulls, assert_unique

# In pytest
def test_data_quality():
    assert_schema_match(df, {'id': 'int64', 'email': 'object'})
    assert_no_nulls(df, ['id', 'email'])
    assert_unique(df, ['id'])

Dependencies

pandas

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