Skill UI

This skill helps owners find complementary cofounders or project partners when explicitly requested. It assesses the owner's own profile using evidence-based vectors like innovation and scaling, comparing only approved own-owner profiles. It requires explicit consent before publishing or contacting others, strictly avoiding inference of sensitive traits.

Learn to operate GPU-backed Kubernetes clusters for AI inference and training workloads. Covers NVIDIA GPU Operator installation, device plugin configuration, MIG partitioning for resource sharing, and time-slicing for non-MIG GPUs. Includes GPU-aware autoscaling, health monitoring with DCGM and Prometheus, and troubleshooting scheduling or driver issues. Essential for production AI infrastructure management.

Provision, configure, and monitor NVIDIA GPU servers for AI inference and training workloads, including driver/CUDA installation, Docker GPU support, MIG partitioning, and health monitoring.

Estimate the required memory for inference of Safetensors or GGUF models from the Hugging Face Hub using HTTP Range requests, without downloading weights. Supports optional KV cache estimation for LLMs and VLMs.

Run Python workloads on Hugging Face Jobs with managed CPUs, GPUs, TPUs, secrets, and Hub persistence. Execute batch inference, training, or recurring tasks without local infrastructure.

This skill guides users on running evaluations against models on the Hugging Face Hub using local hardware. It covers using inspect-ai and lighteval frameworks with local inference backends like vllm, Transformers, or accelerate. It includes task selection, smoke testing, and hardware guidance for different model sizes. It does not cover remote orchestration or publishing results to the community workflow.

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. The skill generates a custom interactive app with the right controls, loading, and inference recipe, then publishes it as a private Space.

Build, deploy, and maintain machine-learning applications on Hugging Face Spaces using Gradio, Docker, or Static SDKs, with ZeroGPU and dedicated hardware, model loading, debugging, and inference providers.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

Design and operate production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance for cloud and self-hosted inference.

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation. Use when building an always-on private AI inference server on Apple Silicon.