Skill UI

This comprehensive benchmark suite evaluates the performance of Vision-Language Models (VLM) on complex video anomaly detection tasks using the SmartHome-Bench dataset. It covers a wide range of real-world smart home scenarios, including security breaches, elderly fall detection, and unusual pet behavior. Unlike single-frame analysis, this benchmark requires deep multi-frame video understanding, making it a robust tool for testing the advanced AI capabilities of video monitoring systems.

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 comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

A comprehensive reference architecture for building scalable, cost-effective, and robust AI services. This system manages the full AI lifecycle, including intelligent model routing (for quality/cost trade-offs), response caching (using Redis), asynchronous batch processing, and fine-tuning pipeline management via Together AI's OpenAI-compatible API. Ideal for enterprise applications requiring high availability across multiple open-source models.

A comprehensive guide to implementing production-ready patterns for interacting with the Together AI API. This includes a singleton client for seamless OpenAI-compatible integration, robust error wrappers with retry logic, and a fluent request builder. It covers best practices for LLM inference, model setup, and handling diverse response types (chat, embedding, fine-tuning).

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 advanced search strategies for tracking Asian music, including K-pop, J-pop, and Filipino genres. It details how to construct highly specific queries by incorporating regional keywords, understanding idol comeback cycles, and utilizing cultural details (such as zodiac or national identity) to narrow down search results and avoid generic noise.

This comprehensive protocol mandates the use of structured skills (tools) at the very beginning of every conversation or response. It establishes a strict, non-negotiable workflow, requiring the AI agent to check for skill applicability even if the task appears simple or direct. It defines skill priority, platform-specific usage methods, and ensures systematic, disciplined tool application to enhance consistency and reliability.

This skill provides high-performance, real-time object detection using the Intel OpenVINO runtime. It operates via a Docker container, ensuring cross-platform compatibility. Supports detection across various Intel accelerators (NCS2, iGPU, Arc GPU) and CPUs. Users can customize parameters like confidence threshold, target classes, processing FPS, and model precision to optimize performance for specific hardware and accuracy needs.

This tool reads your local GitHub Copilot CLI conversation history using the specialized `vardoger` CLI. It analyzes your behavioral patterns, coding conventions, and usage style to generate a personalized instructions block. This block is written directly to your local Copilot configuration file, allowing the assistant to adapt its guidance and suggestions specifically to your unique habits and preferences. The process runs entirely offline, ensuring maximum privacy.

A comprehensive audio generation skill designed for professional multimedia content creation. It handles three types of audio output: music (jingles, beds), voiceovers (TTS), and sound effects (SFX). The skill intelligently routes requests to advanced models like Suno V5, Udio, MiniMax TTS, and ElevenLabs, producing a single, high-quality MP3 or WAV file ready for use in marketing, video, or podcast production.

Utilize Gemini's advanced AI capabilities for broad and comprehensive literature discovery. Unlike traditional keyword-based APIs (like arXiv or Semantic Scholar), this tool decomposes research topics into multiple angles, aliases, and sub-problems, providing a deep, multi-faceted survey of academic literature, including code availability and recent preprints.