인공지능

huggingface

Hugging Face 논문 발행 스킬은 Hugging Face Hub에서 연구 논문을 발행하고 관리하는ための AI 에이전트 스킬입니다

huggingface

Hugging Face 비전 트레이너 스킬, Hugging Face Jobs 클라우드 GPU에서 비전 모델을 훈련하고 미세 조정합니다.

huggingface

Hugging Face 논문 페이지 스킬은 AI 연구 논문을 검색하고 읽는ための AI 에이전트 스킬입니다

huggingface

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

huggingface

Hugging Face 커뮤니티 평가 스킬은 로컬 하드웨어에서 Hugging Face Hub 모델 평가를 실행하는 데 사용되는 AI 에이전트 스킬입니다

huggingface

Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

huggingface

Hugging Face 데이터셋 뷰어 스킬, 데이터셋 탐색 및 추출을 지원하며 페이지네이션, 검색 및 필터링을 제공합니다

huggingface

Hugging Face Hub CLI（hf）は 저장소, 모델, 데이터셋, 공간을 다운로드, 업로드, 관리하기 위한 툴입니다.

huggingface

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in Node.js and browsers (with WebGPU/WASM) using pre-trained models from Hugging Face Hub.

huggingface

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.

huggingface

Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.

huggingface

This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.