localization-testing

This localization testing skill provides a comprehensive framework for validating multi-language and multi-region software support. It is designed for QA engineers and developers tasked with launching products in global markets or maintaining complex, multi-language applications. By integrating with browser-based testing engines, the skill ensures that localized experiences meet rigorous technical and cultural standards before deployment. It moves beyond simple string checks by enforcing RTL (Right-to-Left) design integrity, correct currency and date formatting, and robust Unicode handling.

• Automated translation coverage verification to identify missing strings or untranslated placeholders across all defined locales.

• Dynamic locale-specific format testing for dates, times, currencies, and numeric inputs based on local conventions (e.g., US vs. UK vs. JP formats).

• Advanced RTL layout validation for languages like Arabic and Hebrew, ensuring UI elements, icons, and text directions flip correctly.

• Comprehensive character encoding and Unicode support testing, including emoji and complex script rendering in database and input fields.

• Integrated visual regression testing for language-specific UI components, detecting character expansion or text overflow issues.

• Automated detection of hardcoded strings to ensure total externalization for future localization efforts.

• When executing tests, define the target locales, URLs, and specific check-types (translations, formats, RTL, unicode) to receive a structured summary of issues.

• Use in conjunction with qe-browser fleet skills to handle visual diffs, especially for layout-heavy RTL pages.

• Expected inputs include a base URL, a defined array of locale codes (e.g., en-US, fr-FR, ar-SA), and the specific validation modules to run.

• The skill provides machine-readable output in JSON format, allowing for direct integration into CI/CD quality gates, deployment advisory systems, or automated bug reporting pipelines.

• Prioritize testing with real-world language samples rather than just translation file keys to catch context-dependent issues and cultural appropriateness errors.