Skill UI

This skill generates complete, production-grade client SDKs and API wrapper libraries for any REST API, regardless of the language (Python, TypeScript, etc.). It handles boilerplate code like authentication, retry logic (exponential backoff), data modeling, and structured resource management, saving significant development time. Use it whenever you need to consume a REST API via code.

This skill generates robust, production-grade Appium automation scripts for both Android and iOS. It supports multiple languages (Java, Python, JavaScript) and guides users through best practices for cross-platform testing, including proper locator strategies, explicit waiting, and complex gestures. Ideal for automating mobile QA processes on local emulators or cloud real device farms.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

This skill assists in generating robust Behavior Driven Development (BDD) tests. It supports writing feature files using Gherkin syntax (Given/When/Then) and automatically generates corresponding step definitions in multiple languages, including Java, JavaScript, and Ruby. Ideal for structuring and automating end-to-end functional tests.

This powerful skill provides an interactive debugger interface, allowing developers to set breakpoints, step through source code line by line, inspect live variable states, and trace the entire call stack. It is essential for diagnosing complex bugs, understanding unexpected program flow, or determining the root cause when standard logging fails. It supports various languages via the DAP protocol.

This foundational skill guides the establishment and maintenance of a comprehensive developer audience context. It helps map out detailed developer personas, identify core pain points, analyze current alternatives, and determine key technical differentiators. By structuring this context across 10 critical sections, it ensures all subsequent developer marketing materials are highly targeted and speak the exact language of the target user.

A comprehensive guide and skill for deploying large language models (LLMs) locally using the llama.cpp framework and GGUF quantization format. It covers searching the Hugging Face Hub, selecting optimal quantizations (e.g., Q4_K_M), running local servers, and achieving OpenAI-compatible API endpoints. Supports various hardware backends including CPU, Mac Metal, CUDA, and ROCm. Ideal for developers needing offline, private LLM inference.

This skill automatically generates fully structured, import-ready Postman Collection v2.1 JSON files. It can interpret both natural language descriptions of REST APIs (endpoints, methods, parameters) and raw cURL commands. Use it whenever you need to quickly set up a comprehensive API testing suite in Postman without manual JSON construction, ensuring all variables and environments are properly extracted.

Riffkit analyzes the emotional structure, pacing, and successful 'formula' of a winning short video (like a viral TikTok). It then regenerates an entirely new, original video using this proven formula, customized with your product, character, and desired language (English or Spanish). This skill is ideal for creating high-quality, scalable User-Generated Content (UGC) ad creatives or marketing promos for platforms like TikTok and Meta.

A comprehensive skill for generating production-grade, robust automation scripts and end-to-end tests using Selenium WebDriver. Supports multiple languages (Java, Python, JS, C#, etc.) and execution environments, including local machines and TestMu AI cloud for cross-browser compatibility. It guides users on best practices like explicit waits, Page Object Model (POM), and stable locator strategies, acting as a senior QA automation architect.

A comprehensive guide for training or fine-tuning advanced sentence-transformer models. This skill assists in selecting the correct architecture—including Bi-encoders (SentenceTransformer), CrossEncoders (for reranking), and SparseEncoders—for various Natural Language Processing (NLP) tasks. Use cases include semantic retrieval, similarity matching, document clustering, and pair-wise classification.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.