Skill UI

A comprehensive guide covering Internationalization (i18n) and Localization (L10n) best practices. It details how to build applications that can support multiple languages, regions, and text directions (RTL). Covers implementation patterns for React, Next.js, and Python, along with essential guidelines for handling date/number formats and managing translation keys.

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.

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.

Provides a comprehensive, production-ready reference architecture for integrating Lokalise Translation Management System (TMS) into web applications. This guide covers the entire end-to-end translation lifecycle, including best-practice project layouts, file organization for i18n, CI/CD workflows (build-time vs. OTA paths), and necessary API client implementations. Use this when establishing new multi-language support or standardizing localization practices across a team.

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

This comprehensive guide details how to optimize the Replit development environment. It covers setting up run commands, implementing hot reloading for various languages (Node.js, Python), configuring ports, managing development vs. production databases, and leveraging the Replit Agent for powerful AI-assisted coding. Use this guide when setting up a new project or refining your entire dev workflow.

A comprehensive guide and setup for building a fast, reliable local development workflow for language learning applications. It includes setting up mock tutor responses for offline testing, generating audio test fixtures using TTS, and implementing a debug mode for detailed pronunciation scoring analysis. Ideal for engineers setting up a new Speak-based integration or refining existing test pipelines.

This guide outlines essential security best practices for integrating the Speak API. It covers critical areas like securing API keys using secrets managers, ensuring audio data privacy by deleting local files, and maintaining compliance with major regulations such as COPPA and FERPA. Use this knowledge when building production-ready, secure language learning applications.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

This file serves as the dedicated translation resource for the MCP Server Patterns library. It is used to localize key strings and texts to ensure accurate user experience across different languages. Developers should refer to the original skill file for the core English content.

This skill automates the comprehensive localization and translation of an entire MkDocs documentation suite. It translates all Markdown files from source English folders into a specified target language, preserving the exact folder structure and Markdown formatting. Additionally, it updates include references and modifies the core mkdocs.yml configuration file to properly support the new locale, ensuring a seamless multilingual documentation setup.

Design, analyze, and refine Azure infrastructure through natural language prompts, generate interactive architecture diagrams locally with bundled scripts, and produce deployable Bicep templates to automate infrastructure-as-code workflows.