Skill UI

Handles user prompts to send local files or images by emitting the required Weixin protocol block so the app can deliver them directly; report success only when the block and target file exist to avoid false claims.

This comprehensive guide provides a structured set of prompts to manage the entire lifecycle of upgrading a legacy .NET solution. It covers everything from initial project classification and dependency compatibility review to code modernization (e.g., async conversion), updating CI/CD pipelines, and defining robust testing and deployment strategies. Ideal for large-scale migration of older .NET applications to modern frameworks like .NET 8.

This tool generates a complete, production-ready project scaffold for a Model Context Protocol (MCP) server written in Ruby. It includes the necessary file structure, gem dependencies, Rake tasks, and boilerplate code for defining tools, prompts, and resources, accelerating backend development for AI applications.

This tool scaffolds a complete, production-ready Model Context Protocol (MCP) server project written in Rust. It utilizes the official `rmcp` SDK to ensure full compatibility. The generated structure includes modules for handling logic, defining tools, managing prompts, and providing resources. Users can configure different transports (stdio, SSE, HTTP) and immediately run the server for integration testing or production use.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

This skill provides a comprehensive framework for optimizing, improving, and debugging Large Language Model (LLM) prompts. It leverages production trace data, system evaluations, and human annotations to extract prompts from spans. Users can run a data-driven optimization loop, ensuring the AI responses meet desired quality standards. Ideal for prompt tuning, system prompt refinement, and improving overall AI output quality.

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.

Interactive workflow that interrogates scope, deliverables, constraints, and success criteria while guiding users to refine task prompts; copies the final markdown with Joyride clipboard commands and never writes code.

Generates customizable prompts to scan a codebase, identify high-quality exemplars, and document patterns across languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable depth, categorization, and documentation formats.

Generates Excalidraw diagrams from natural language prompts, covering flowcharts, relationship maps, mind maps, architecture, data flow, swimlane, class, sequence, and ER diagrams for communicating processes and systems.

Transforms Finnish text by spotting 26 AI markers (12 language-specific, 14 universal) and rewriting them to match native rhythms, so prompts or .md/.txt content read like human-written copy.

This tool generates a complete, production-ready PHP Model Context Protocol (MCP) server project. It automatically scaffolds the entire structure, including tools, resources, prompts, and comprehensive tests, utilizing the official PHP SDK. It provides a standardized, robust starting point for developers building AI-integrated backend services in PHP.