Skill UI

This AI-powered skill enables users to upscale and enhance the resolution of images and videos. It improves visual quality by increasing pixel density and clarity through advanced machine learning models. Suitable for content creators, developers, and professionals needing higher resolution media assets for various applications.

Generate workflow JSON files for chaining AI models. This skill provides guidance and patterns for creating structured workflows that link multiple AI models in sequence, enabling complex multi-step AI pipelines.

Build with the Gemini Interactions API for text, chat, multimodal generation, streaming, managed or background agents, function calling, structured output, and generateContent migrations. Includes current models, agents, SDKs, and critical usage rules.

Generate and edit images and videos using Google's Gemini media models (Nano Banana 2/Pro, Gemini Omni Flash) with cost-approval gates, reference-image support, and detailed logging per call. Supports on-brand generation from reference sets and multi-image fusion for consistent character series.

This skill evaluates content for AI citability based on research from Princeton and Georgia Tech. It scores content on answer block quality, self-containment, and structural readability to optimize visibility in AI-generated responses. Ideal for SEO and GEO strategies to ensure AI models extract and cite your content effectively.

This skill enables AI assistants to interact with the Hugging Face Hub via Model Context Protocol (MCP). Users can search for models, datasets, and papers, retrieve repository details, fetch documentation, and execute compute jobs on GPU or CPU. It also allows leveraging Gradio Spaces as functional AI tools. Ideal for developers and researchers needing direct access to the HF ecosystem for model discovery, training, and deployment workflows.

Estimates VRAM/memory required for inference of Safetensors or GGUF models from Hugging Face Hub, using HTTP range requests without downloading weights. Supports KV cache estimation with experimental flag.

The Hugging Face Hub CLI (hf) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more. Supports authentication, cache management, and bucket operations.

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Handles backend selection, local GPU evals, and choosing between vLLM, Transformers, and accelerate.

Train or fine-tune language and vision models using TRL or Unsloth on Hugging Face Jobs infrastructure. Supports SFT, DPO, GRPO methods and GGUF conversion for local deployment. Requires Pro account and HF_TOKEN for Hub saving. Optimized for cloud GPU training without local setup.

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles. Ideal for AI engineers and researchers.

Look up Hugging Face paper pages as markdown and query the papers API for structured metadata including authors, linked models, datasets, Spaces, GitHub repos, and project pages. Ideal for summarizing or analyzing AI research papers from hf.co or arXiv URLs and IDs.