What are Datadir tests in the context of Keboola?

Datadir tests are the primary functional testing method for Keboola components. They mirror the production environment's directory structure (in/out tables, config, state) to verify the complete component workflow.

How does this skill handle external API dependencies?

The skill uses mocking patterns (specifically unittest.mock) to simulate API responses, allowing you to test components in isolation without needing real credentials or internet access.

Can I test incremental loading and state management?

Yes, the skill includes specific patterns for setting up in/state.json and verifying that out/state.json is updated correctly for subsequent runs.

Does it support CSV manifest validation?

Absolutely. It provides assertions to verify that manifest files are generated with the correct primary keys, incremental flags, and table metadata.

What testing frameworks does this skill use?

It is optimized for use with Python's unittest and pytest frameworks, utilizing the uv package manager for running tests and managing dependencies.

Keboola Component Testing Expert

Name: Keboola Component Testing Expert
Author: keboola

bykeboola

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Seguridad y Pruebas

Generates comprehensive datadir, unit, and integration tests for Keboola Python components to ensure production-grade reliability.

The Keboola Component Testing Expert is a specialized skill designed to automate the creation and maintenance of robust test suites for Python-based data components. It prioritizes Datadir tests that mirror actual production environments, ensuring that configuration handling, state management, and manifest generation work perfectly before deployment. By providing standardized patterns for unit testing complex logic and mocking external API dependencies, this skill helps developers maintain high-quality data pipelines and reduce runtime errors in the Keboola ecosystem.

Características Principales

01Automated Datadir test generation including config, input, and manifest files

02Comprehensive unit testing patterns for isolated business logic validation

03API integration testing with pre-configured mocking and assertion patterns

04Validation of CSV output formats, encoding, and state persistence

05Standardized test suite reporting compatible with pytest and uv

067 GitHub stars

Casos de Uso

01Developing a new Keboola Python extractor with full functional and unit test coverage

02Refactoring component logic while maintaining safety through mocked API responses

03Adding incremental load validation to ensure state files update correctly across runs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add keboola/ai-kit test-component

For use in Claude.ai and ChatGPT

Download Skill