Skill UI

A comprehensive Rust client library for integrating with Azure Service Bus. It allows developers to send and receive messages reliably using both point-to-point queues (for competing consumers) and publish-subscribe topics. This skill is essential for building robust, event-driven, and highly available distributed applications in Rust that require durable message passing within the Azure cloud environment.

This skill guides the implementation of advanced SEO strategies tailored specifically for technical audiences and developers. It covers specialized techniques such as optimizing for error messages, researching high-intent technical long-tail keywords, creating structured how-to guides, and developing objective tool comparison content. Use this when targeting developers who search with precise technical intent, needing solutions that go beyond standard documentation.

A comprehensive guide to designing effective free tiers, freemium models, and free trials for developer tools. It teaches how to create usage limits and feature gates that encourage natural conversion to paid plans without causing developer resentment or hindering initial adoption.

Create highly aesthetic, animation-rich HTML presentations from scratch or convert existing PowerPoint files (PPT/PPTX). This tool focuses on generating distinctive, professional web decks with fixed 16:9 ratios. It supports low-density for keynote talks or high-density for detailed reports, ensuring visual impact and zero dependencies.

Use this comprehensive skill for interacting with the Gemini API. It supports a wide range of advanced AI tasks, including text generation, multi-turn chat, multimodal understanding, image/video creation, function calling, and structured output. Provides modern SDKs for robust application development in Python and JavaScript/TypeScript.

This guide details best practices for implementing robust, type-safe GraphQL queries and mutations. It covers using separate .gql files, running code generation tools, and implementing mandatory error handling (onError) and loading states. Use this when building complex, reliable data fetching layers with Apollo Client.

This skill provides comprehensive access to the Hugging Face Hub ecosystem via MCP server tools. Users can search for state-of-the-art models, datasets, and research papers, retrieve detailed repository information, fetch documentation, run compute jobs on GPU/CPU, and interact with Gradio Spaces as fully functional AI tools. Ideal for ML development, research, and application prototyping.

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.

A comprehensive utility for building reusable, command-line scripts and utilities to interact with the Hugging Face API. It enables complex data workflows by allowing users to chain, pipe, and process data fetched from models, datasets, and various endpoints. Ideal for automation, enrichment, and advanced data pipelines.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

This skill guides the entire lifecycle of repeatable AI agent loops. Use it to discover recurring work patterns within codebases, find established solutions in a library, or interview users to craft bounded, effective loops. It supports auditing, repairing, running, and preparing AI workflows for publication, treating the loop as a robust feedback system.

This skill helps diagnose performance bottlenecks, slow jobs, and expensive queries across various data platforms (Airflow, dbt, Databricks). It uses a tiered investigation approach, starting with broad discovery to pinpoint problematic jobs or queries, bridging to affected tables, and finally drilling down to identify root causes, latency trends, and change-related regressions.