Skill UI

This file serves as the localization resource for the Liquid Glass Design skill, specifically targeting Japanese translation. Please note that this file is currently under translation development. For the complete functionality and core documentation, refer to the original skill file.

llama.cpp is a pure C/C++ library designed for efficient LLM inference, optimized to run on resource-constrained environments. It excels in scenarios where high-end NVIDIA GPUs (CUDA) are unavailable, making it ideal for Apple Silicon Macs, AMD/Intel GPUs, or edge deployment devices like Raspberry Pi. It supports GGUF quantization formats, enabling significant memory reduction and speed improvements for local, cross-platform AI applications.

A comprehensive guide to integrating Lokalise into Continuous Integration/Continuous Deployment (CI/CD) workflows using GitHub Actions. This setup automates the entire translation lifecycle: uploading source strings on code pushes, downloading the latest translations during builds, and running PR checks to ensure translation coverage across all required locales. It eliminates manual file management, keeping code, translations, and CI pipelines perfectly synchronized.

A comprehensive workflow tool for managing the entire localization process using Lokalise. It handles downloading translated files (JSON, XLIFF, PO), reviewing translation status, updating keys, and integrating the output directly into development applications. Ideal for CI/CD pipelines and developers needing to sync localized content efficiently.

This comprehensive guide provides step-by-step instructions to optimize localization spending on Lokalise. Learn how to audit current usage across projects and languages, minimize per-seat costs by managing contributor access, maximize Translation Memory (TM) hits, and efficiently triage Machine Translation (MT) to keep your localization projects well within budget.

This skill provides comprehensive handling for Lokalise translation data, covering the entire localization lifecycle. It facilitates creating keys with rich metadata, managing versions using immutable snapshots, isolating work with feature branches, and generating various export formats (JSON, XLIFF). It is essential for handling sensitive translation data, ensuring PII compliance, and managing scalable internationalization within development pipelines.

This skill provides a comprehensive guide for integrating Lokalise into CI/CD pipelines. It outlines how to automate the download of the latest, reviewed translations during the build stage, ensuring that production environments always ship the most current localized content. Ideal for setting up deployment workflows on platforms like Vercel, Netlify, and GitHub Actions.

This comprehensive guide provides an end-to-end walkthrough for integrating with the Lokalise platform. It demonstrates core localization tasks, including listing projects, creating new multilingual projects, adding translation keys, and updating translations for multiple languages using both the Node SDK and the CLI. Ideal for developers establishing robust internationalization (i18n) pipelines.

A structured playbook for rapidly diagnosing and resolving production incidents related to Lokalise localization services. It guides users through essential quick diagnostics (API health, token validity, rate limits) and provides a decision tree for common failure modes, such as missing or stale translations, API outages, and upload failures, ensuring swift mitigation and postmortem readiness.

This guide provides a comprehensive walkthrough for integrating Lokalise into any project. It covers installing the Node.js SDK (@lokalise/node-api) and the local command-line interface (lokalise2). Crucially, it details the process of generating, setting, and verifying API tokens or OAuth credentials, ensuring secure and functional communication with the Lokalise platform. Use this when setting up a new localization integration or refreshing existing credentials.

This guide provides a comprehensive local development environment setup for Internationalization (i18n) using Lokalise. It details configuring the project structure, implementing CLI scripts for synchronizing source strings (push) and translations (pull), setting up file watching for hot reloading, and generating mock translations for robust offline testing. Ideal for developers managing multi-language applications.

A comprehensive deep dive guide for migrating translation memory (TM) and localization content from competing Translation Management Systems (TMS) like Crowdin, Phrase, or POEditor into Lokalise. This process involves exporting data from the source platform, transforming key variables and file structures to match Lokalise conventions, and finally performing a bulk API upload to establish a new, centralized localization repository. Perfect for re-platforming or migrating existing translation pipelines.