Can this skill help with database integration testing?

Yes, it provides implementation patterns for using testcontainers to spin up real, isolated database instances like Postgres for high-fidelity integration tests.

Who should use the Data Quality & Testing skill?

It is designed for Data Engineers and Backend Developers who need to maintain high reliability in data-intensive applications and automated pipelines.

What testing frameworks are included in this skill?

This skill primarily focuses on pytest for unit and integration testing, Pydantic for data validation, and Great Expectations for specialized data quality assertions.

Is mocking external services covered?

Yes, it includes examples for using the 'responses' library to mock HTTP requests and 'unittest.mock' for simulating internal service dependencies.

How does this skill handle data validation?

It uses Pydantic for runtime schema enforcement and Great Expectations for checking data distributions, uniqueness, and nullity within dataframes or SQL tables.

Data Quality & Testing

Name: Data Quality & Testing
Author: pluginagentmarketplace

bypluginagentmarketplace

•

セキュリティとテスト

Ensures data integrity and software reliability through robust testing frameworks, schema validation, and automated quality assurance patterns.

This skill provides a comprehensive toolkit for implementing production-grade testing strategies in data-heavy environments. It offers standardized patterns for unit testing with pytest, rigorous schema validation using Pydantic, and specialized data quality checks with Great Expectations. Ideal for data engineers and backend developers, it covers the full spectrum of testing from mocking external API services to running realistic integration tests with Dockerized databases via testcontainers, ensuring that ETL pipelines and data processing tools remain resilient and bug-free.

主な機能

012 GitHub stars

02Unit testing patterns using pytest fixtures and parametrization

03API mocking and external service simulation with responses

04Automated data quality profiling with Great Expectations

05Data validation and type enforcement with Pydantic models

06Integration testing using real database containers (testcontainers)

ユースケース

01Simulating edge cases and error handling for complex backend service integrations

02Implementing automated quality gates in CI/CD pipelines for data products

03Validating ETL pipeline outputs against business logic and schema constraints

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pluginagentmarketplace/custom-plugin-data-engineer testing-quality

For use in Claude.ai and ChatGPT

Download Skill