Skills from Hugging Face’s repositories that passed our trust, licence and security gates. Each keeps its author’s attribution.
| Repository | Tier | Verdict | Authorship | Checked | Skills |
|---|---|---|---|---|---|
| huggingface/skills | Vendor | Approved · created 2025-11-24, pushed 2026-10-30, 11292 stars | Written in-house | 2026-11-07 | 26 |
Files are mirrored only where the licence allows redistribution; otherwise we link to the source.
Hugging FaceOfficial
Deploys SageMaker endpoints with standardized autoscaling, CloudWatch monitoring, and resource tagging.
Hugging FaceOfficial
Plan and coordinate the deployment of machine learning models to Amazon SageMaker.
Hugging FaceOfficial
A comprehensive toolkit for publishing, managing, and linking research papers within the Hugging Face ecosystem.
Hugging FaceOfficial
A comprehensive guide to building, deploying, and maintaining machine learning applications on Hugging Face Spaces.
Hugging FaceOfficial
Trackio is a lightweight experiment tracking library for logging, visualizing, and alerting on machine learning training metrics.
Hugging FaceOfficial
Automates the discovery and validation of AWS SageMaker execution roles to prevent deployment failures.
Hugging FaceOfficial
Build interactive web UIs and machine learning demos in Python using the Gradio library.
Hugging FaceOfficial
Identify and compare top-performing AI models based on benchmark leaderboards and hardware constraints.
Hugging FaceOfficial
Integrate Hugging Face paper pages to retrieve, analyze, and manage AI research paper metadata and content.
Hugging FaceOfficial
Run machine learning models directly in JavaScript and TypeScript environments.
Hugging FaceOfficial
Standardized procedure for creating isolated Python environments for AWS SageMaker development.
Hugging FaceOfficial
A structured workflow for training and fine-tuning sentence-transformers models including bi-encoders, cross-encoders, sparse, and multi-vector architectures.
Hugging FaceOfficial
Develop and optimize Gradio applications for Hugging Face Spaces using ZeroGPU hardware.
Hugging FaceOfficial
Automates the creation and deployment of Gradio demos for Hugging Face LoRA models.
Hugging FaceOfficial
Estimate memory requirements for Hugging Face Safetensors and GGUF models without downloading them.
Hugging FaceOfficial
Train and fine-tune language models on Hugging Face infrastructure using TRL or Unsloth.
Hugging FaceOfficial
Integrate with the Hugging Face Dataset Viewer API to explore, filter, and extract metadata from hosted datasets.
Hugging FaceOfficial
A guide for post-training LLMs using the TRL (Transformers Reinforcement Learning) library for SFT, DPO, GRPO, KTO, and reward modeling.
Hugging FaceOfficial
Standardized procedure for selecting and retrieving the correct SageMaker serving container image URI for HuggingFace model deployments.
Hugging FaceOfficial
Run local model evaluations on Hugging Face Hub models using inspect-ai and lighteval.
Hugging FaceOfficial
Builds reusable scripts and CLI tools to interact with the Hugging Face API and Hub infrastructure.
Hugging FaceOfficial
A mandatory pre-task routine to identify and validate local AWS configuration, credentials, and IAM principal type before executing cloud operations.
Hugging FaceOfficial
A guide for discovering, selecting, and running GGUF models locally using llama.cpp.
Hugging FaceOfficial
A comprehensive workflow for training and fine-tuning vision models on Hugging Face Jobs cloud infrastructure.