skills

hf-cli

manage Hugging Face Hub resources via CLI

hf-cloud-aws-context-discovery

discover local AWS environment context

hf-cloud-python-env-setup

set up Python environments for AWS

hf-cloud-sagemaker-deployment-planner

plan model deployments to Amazon SageMaker

hf-cloud-sagemaker-iam-preflight

configure SageMaker IAM roles

hf-cloud-sagemaker-production-defaults

create production-ready SageMaker endpoints

hf-cloud-serving-image-selection

select SageMaker serving containers

hf-mcp

access Hugging Face Hub via MCP

hf-mem

estimate memory for Hugging Face models

huggingface-best

find and compare Hugging Face models

huggingface-community-evals

run local evaluations for Hugging Face models

huggingface-datasets

browse and search Hugging Face datasets

huggingface-gradio

build web UIs and demos with Gradio

huggingface-llm-trainer

train and fine-tune LLMs with TRL

huggingface-local-models

run Hugging Face models locally

huggingface-lora-space-builder

build and publish Gradio demos for LoRAs

huggingface-paper-publisher

publish research papers on Hugging Face Hub

huggingface-papers

research and read Hugging Face papers

huggingface-spaces

build and deploy applications on Hugging Face Spaces

huggingface-tool-builder

build tools using the Hugging Face API

huggingface-trackio

track and visualize ML training experiments

huggingface-vision-trainer

train and fine-tune computer vision models

huggingface-zerogpu

configure and deploy Hugging Face ZeroGPU spaces

transformers-js

run machine learning models in JavaScript