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testing-patterns

Provide unit, data, integration, performance, and QA testing patterns for dbt-based data models.

4|1|Updated Feb 2, 2026
Search Tags:#testing#analytics#qa#dbt#unit-tests#data-quality#integration-tests
One-click install
npx skills add https://github.com/dylpickledev/claude-analytics-framework --skill testing-patterns-dylpickledev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: testing-patterns
Source: https://github.com/dylpickledev/claude-analytics-framework/tree/main/.claude/skills/reference-knowledge/testing-patterns
Command: npx skills add https://github.com/dylpickledev/claude-analytics-framework --skill testing-patterns-dylpickledev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analytics teams often struggle with inconsistent testing practices across models, pipelines, and dashboards, leading to data quality issues and delayed deliveries. This Skill provides a structured reference of testing patterns aligned to the Analytics Development Lifecycle (ADLC), offering guidance to implement reliable tests at every stage.

Core Features & Use Cases

  • ADLC testing alignment guides for unit, data, integration, and performance tests.
  • Data quality frameworks, schema checks, and business logic verifications with practical examples.
  • End-to-end testing workflows, including dbt testing commands, CI/CD considerations, and QA readiness criteria.
  • Documentation and memory markers to capture testing decisions and lessons learned for future reuse.

Quick Start

Run the ADLC testing patterns on your dbt project and execute the recommended test suite.

Frequently Asked Questions about testing-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up dbt unit tests for data pipelines?▼

dbt unit tests validate individual transformations by isolating business logic. This Skill provides standardized analytics testing patterns to implement unit, data, and integration tests for data pipelines.

What is the best way to structure data quality checks across analytics models?▼

The best way to structure data quality checks is aligning them with the Analytics Development Lifecycle. This framework provides reusable patterns for schema checks, business logic verifications, and performance tests.

Does this testing framework include integration and performance test patterns?▼

Yes, this testing framework includes integration and performance test patterns. It covers the full Analytics Development Lifecycle scope, providing practical command examples for typical data environments.

How do I integrate dbt testing commands into a CI/CD pipeline?▼

To integrate dbt testing commands into CI/CD, follow end-to-end testing workflows that define QA readiness criteria. This Skill provides practical command examples to execute recommended test suites during continuous integration.

When do I need analytics testing patterns for performance and QA?▼

You need analytics testing patterns when inconsistent practices cause data quality issues and delayed deliveries. This framework provides structured reference guides for implementing performance and QA tests across data pipelines.

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