Skill UI

A comprehensive framework designed to structure and automate PyTorch deep learning workflows. It simplifies boilerplate code by organizing models into LightningModules, automating training loops via the Trainer, and providing robust support for multi-GPU/TPU scaling (DDP, FSDP, DeepSpeed), data handling (DataModules), and best-in-class experiment logging (W&B, MLflow). Ideal for professional, scalable deep learning project development.

A comprehensive guide for optimizing cloud GPU expenditures on CoreWeave. Learn strategies like right-sizing instances, implementing scale-to-zero for development environments, and using quantization techniques (e.g., AWQ, GPTQ) to fit large models onto smaller, cost-effective GPUs, ensuring maximum efficiency and budget adherence for AI/ML workloads.

This debug bundle provides development patterns and SDK integration for RunwayML, a powerful AI video generation and creative platform. Users can leverage this bundle to perform tasks like image-to-video conversion and advanced content creation. It includes structured examples and robust error handling, making it ideal for developers needing to integrate professional-grade AI video capabilities into their applications.

This skill provides a development loop for RunwayML, an advanced creative AI platform. It allows users to programmatically generate high-quality videos from images or text prompts using the Python SDK. Ideal for integrating Runway's capabilities into local development workflows, enabling advanced creative video production, editing, and media manipulation.

This debug bundle provides comprehensive guidance for working with Together AI's OpenAI-compatible API. It supports critical LLM lifecycle tasks, including inference, fine-tuning, and model deployment for over 100 open-source models (e.g., Llama, Mixtral). It also includes detailed troubleshooting steps and solutions for common API errors (like 401 Unauthorized or 429 Rate limit), ensuring smooth development.

A comprehensive guide on how to write, organize, and optimize skills for AI agents using a Test-Driven Development (TDD) approach, focusing on Claude Search Optimization (CSO) and token efficiency.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

This system creates and registers high-quality, reusable synthetic voices (voice clones) from local audio samples. It operates under a strict, consent-first workflow, requiring explicit speaker consent, detailed use permissions, and source verification before cloning. It is designed for professional Text-to-Speech (TTS) applications, IVR systems, or training content development where a consistent, branded voice is essential.