Skill UI

This skill provides a comprehensive guide to setting up full observability for Lokalise integrations. It covers crucial metrics, traces, and alert configurations, monitoring everything from API response times and rate limit consumption to translation progress and webhook reliability. Ideal for developers building robust monitoring dashboards and alerting systems for localization workflows.

This skill provides advanced techniques for optimizing throughput and reducing latency when interacting with the Lokalise API. Learn to implement cursor-based pagination for massive key sets, establish robust local caching strategies using project activity timestamps, and utilize efficient batch operations (up to 500 items) for creating or updating keys. Essential for building scalable, high-performance translation pipelines.

This comprehensive script suite provides a structured, multi-step audit for internationalization (i18n) integrations using Lokalise. It automates crucial checks, including verifying translation coverage across all required locales, detecting key synchronization issues (missing or orphaned keys) between the source code and the Translation Management System (TMS), and validating the format of localized files. Use this utility before any major production deployment or go-live phase to ensure localization readiness.

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.

A comprehensive guide to setting up a local development loop for building and testing MaintainX integrations. This workflow utilizes TypeScript, hot reloading, mock API servers, and automated unit/integration testing. It provides a sandboxed environment for developers to efficiently develop and debug their services.

A comprehensive guide to implementing event-driven integrations with MaintainX. This skill covers setting up webhooks to capture real-time updates for work orders, asset changes, and maintenance notifications. It includes best practices like HMAC signature verification, idempotent processing to prevent duplicate actions, and setting up local development environments.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.

A comprehensive guide to establishing a fast, reproducible local development lifecycle for Mistral AI integrations. This workflow includes setting up project scaffolding, implementing hot reloading with tsx, performing unit tests with Vitest mocking, and executing live integration tests against the Mistral API. Ideal for setting up reliable AI application development environments.

An expert guide for designing, setting up, and optimizing large-scale monorepositories. Masters advanced build systems like Nx, Turborepo, Bazel, and Lerna to manage complex dependency graphs, implement efficient build caching (local and remote), and optimize slow CI/CD pipelines for multi-project development workflows.

Runs build, test, lint, serve, and other tasks in Nx workspaces via run, run-many, and affected flows, honoring the detected package manager and target metadata while exposing filters, parallelism, and caching controls for CI or local workflows.

This guide establishes a comprehensive and rapid development loop for Obsidian plugins. It covers the entire workflow, from setup (cloning templates, symlinking) to advanced features, including running esbuild in watch mode, achieving hot-reloading (via Ctrl+R or automatic methods), deep debugging with Chrome DevTools, and running unit tests with vitest. Essential for developers seeking fast feedback and reliable plugin iteration.

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.