hf-cli | Hugging Face Hub CLI (hf) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. | SKILL.md |
hf-cloud-aws-context-discovery | Discover the user''s local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. | SKILL.md |
hf-cloud-python-env-setup | Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. | SKILL.md |
hf-cloud-sagemaker-deployment-planner | Plan and coordinate the deployment of a model to Amazon SageMaker AI. | SKILL.md |
hf-cloud-sagemaker-iam-preflight | Ensure a usable SageMaker execution role exists before deploying or training. | SKILL.md |
hf-cloud-sagemaker-production-defaults | Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. | SKILL.md |
hf-cloud-serving-image-selection | Pick the right serving container for a SageMaker model deployment and find its current image URI. | SKILL.md |
hf-mem | Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub | SKILL.md |
huggingface-best | Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. | SKILL.md |
huggingface-community-evals | Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. | SKILL.md |
huggingface-datasets | Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics. | SKILL.md |
huggingface-gradio | Build Gradio web UIs and demos in Python. | SKILL.md |
huggingface-llm-trainer | Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. | SKILL.md |
huggingface-local-models | Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. | SKILL.md |
huggingface-lora-space-builder | Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. | SKILL.md |
huggingface-paper-publisher | Publish and manage research papers on Hugging Face Hub. | SKILL.md |
huggingface-papers | Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. | SKILL.md |
huggingface-spaces | Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. | SKILL.md |
huggingface-tool-builder | Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. | SKILL.md |
huggingface-trackio | Track and visualize ML training experiments with Trackio. | SKILL.md |
huggingface-vision-trainer | Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. | SKILL.md |
huggingface-zerogpu | AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. | SKILL.md |
train-sentence-transformers | Train or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker; pair scoring for two-stage retrieval / pair classification), and SparseEncoder (SPLADE, sparse embedding model; for learned-sparse retrieval). | SKILL.md |
transformers-js | Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. | SKILL.md |
trl-training | Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). | SKILL.md |
