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bdd-features

Generate Gherkin feature files for Databricks testing with Given/When/Then scenarios.

Updated May 31, 2026
Search Tags:#test automation#gherkin#databricks#unity catalog#pipeline testing
One-click install
npx skills add https://github.com/thbeh/coding-agents-databricks-apps --skill bdd-features-thbeh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdd-features
Source: https://github.com/thbeh/coding-agents-databricks-apps/tree/main/.claude/skills/bdd-features
Command: npx skills add https://github.com/thbeh/coding-agents-databricks-apps --skill bdd-features-thbeh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, behave, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users write Gherkin feature files for Databricks pipelines and tests, streamlining the process of translating requirements and user stories into executable test cases.

Core Features & Use Cases

  • Gherkin Feature File Generation: Automatically generate well-structured Gherkin .feature files for Databricks workloads.
  • Requirement Translation: Convert requirements, user stories, or existing code into behavior specifications using Given/When/Then syntax.
  • Use Case: For a data engineer who needs to write acceptance tests in Gherkin for Databricks pipelines or apps, this Skill simplifies the creation of feature files for permission verification, pipeline lifecycle testing, and API endpoint validation.

Quick Start

Use the bdd-features skill to generate a Gherkin feature file for your Unity Catalog permission test.

Frequently Asked Questions about bdd-features

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

FAQPage Schema
How do I generate Gherkin feature files for Databricks pipeline testing?▼

You can translate user requirements and stories into Given/When/Then scenarios for Databricks testing. This Skill generates structured Gherkin feature files covering Unity Catalog permissions, Lakeflow pipelines, jobs, notebooks, and Databricks Apps.

What is Gherkin behavior-driven testing used for in Databricks?▼

Gherkin behavior-driven testing in Databricks is used to define executable acceptance criteria for data pipelines. It translates plain language requirements into structured Given/When/Then scenarios for validating Unity Catalog permissions and API endpoints.

Do I need Python and Behave to interpret Gherkin scenarios for Unity Catalog?▼

Yes, you need Python and the Behave library to interpret and execute the generated Gherkin scenarios. These dependencies are required to run the behavior specifications against your Databricks workloads and Unity Catalog permissions.

Can I convert user stories into Given/When/Then scenarios for Databricks Apps?▼

Yes, you can convert user stories into Given/When/Then scenarios for Databricks Apps. The Skill automatically translates provided requirements into well-structured Gherkin feature files suitable for API endpoint validation and app lifecycle testing.

What's the best way to write acceptance tests for Lakeflow pipelines?▼

The best way to write acceptance tests for Lakeflow pipelines is translating requirements into Gherkin feature files. This approach streamlines creating executable test cases by generating structured Given/When/Then behavior specifications automatically.

Are there limitations when using Gherkin for Databricks workload validation?▼

A limitation when using Gherkin for Databricks validation is the strict dependency on Python and the Behave library for interpretation. The generated feature files focus on Databricks-specific contexts like Unity Catalog and Lakeflow pipelines.

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