Skill UI

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 guide for configuring localization services (Lokalise) across multiple deployment environments (development, staging, production). It details two strategies: dedicated projects or single-project branching. Focuses heavily on secure secret management using tools like AWS Secrets Manager, GCP Secret Manager, and HashiCorp Vault, ensuring that environment-specific configurations and translations remain isolated throughout the CI/CD pipeline.

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.

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 provides a local development loop for integrating the OpenEvidence clinical decision support API. Developers can build health-tech tools and quickly iterate using mock evidence queries, citation responses, and clinical summaries. It supports toggling between mock mode for rapid development and sandbox mode for validation against the real platform, ensuring development uses de-identified data.

Outlines guarantees valid JSON/XML/code outputs by applying CFG/FSM-constrained token filtering, Pydantic schemas, and grammar-level sampling while supporting local Transformers, llama.cpp, and vLLM backends for fast inference.

This guide details the recommended pattern for managing per-project plugin settings and state within a development environment. Plugins store their configuration in a dedicated `.claude/plugin-name.local.md` file. This file utilizes YAML frontmatter for structured settings (like enabling flags, retry counts, and parameters) and a Markdown body for additional context or instructions. It provides techniques for reading these settings using Bash hooks, commands, and agents.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

A comprehensive guide to modern React state management patterns. It covers various state types—including local, global, server, and form state—and demonstrates best practices using industry-leading libraries like Redux Toolkit, Zustand, Jotai, and React Query. Use this when building complex, scalable React applications and needing to select the optimal state solution.