Skill UI

This guide provides a comprehensive tutorial on running various AI inference tasks using the Together AI API, which is compatible with the OpenAI standard. It demonstrates key functionalities including chat completions, streaming responses, image generation, and creating embeddings. This is ideal for developers who need to test open-source models, compare performance across different LLMs (like Llama or Mixtral), or integrate powerful generative AI features into their applications using Python or Node.js.

This guide provides comprehensive instructions for setting up and authenticating the Together AI SDK. It covers installation using both Python and Node.js, configuring the necessary API key, and verifying connectivity to utilize the OpenAI-compatible API for powerful open-source model inference and fine-tuning tasks. Ideal for developers starting with Together AI.

This utility handles asynchronous webhook events from Together AI, enabling developers to build robust, event-driven Machine Learning pipelines. Instead of constantly polling the API for status updates (e.g., fine-tuning completion, batch job status, model deprecation), you can subscribe to these webhooks. It includes logic for signature verification, event routing, and idempotency, making it ideal for mission-critical AI workflows.

This guide provides detailed instructions for optimizing cost and implementing voice-to-text API integrations using Wispr Flow. It covers best practices for WebSocket streaming, REST API usage, context awareness (like programming code), and robust error handling, helping developers build advanced voice-powered applications.

A comprehensive voiceover studio that transforms written text into high-quality, professional spoken audio. It supports generating content for podcasts, IVR prompts, educational videos, and product demos. The system handles complex requirements including target locale, specific accents (e.g., British, Mandarin), desired emotion, and pacing, ensuring the resulting audio artifact is natural and contextually accurate. Ideal for content creators and developers needing reliable, multi-lingual narration.

This skill provides a hybrid, persistent, and searchable knowledge memory system specifically designed for enhancing AI agents' capabilities. It allows developers to record, retrieve, and search key knowledge, architectural decisions, and operational memories automatically. Use it to build agents with long-term context and reliable knowledge management, ensuring findings are synced back into project documentation.

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 professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.

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.

The Neon AI Gateway provides a single API endpoint and one credential to access a diverse catalog of frontier and open-source LLMs (including OpenAI, Anthropic, Google Gemini, Meta, Alibaba, and DeepSeek). This unified layer simplifies integration by eliminating the need for separate provider accounts and managing model-specific SDK complexities. It allows developers to switch between models seamlessly by changing only the model name, while also providing crucial branch-scoped isolation for development and CI environments.

This skill provides a detailed, side-by-side comparison of advanced LLM agent evaluation frameworks, such as ruflo and HAL. It helps researchers and developers identify capability gaps—including real code execution, full web browsing, and structured file parsing—and prioritize architectural improvements for robust agent benchmarking and performance tracking.

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.