AI

huggingface

Hugging Face論文出版スキルは、Hugging Face Hub上で研究論文を公開・管理するためのAIエージェントスキル

huggingface

Hugging Faceビジョントレーナースキル、Hugging Face JobsのクラウドGPUでビジョンモデルをトレーニングしてファインチューニングする

huggingface

Hugging Face論文ページスキルは、AI研究論文を検索して読むためのAIエージェントスキルです

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コミュニティ評価スキルは、Hugging Face Hubモデルの評価をローカルハードウェアで実行するためのAIエージェントスキルです

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 データセットビューアー スキル、データセットの探索および抽出をサポート、ページング、検索、フィルタリング

huggingface

Hugging Face Hub CLI（hf）は、リポジトリ、モデル、データセット、スペースをダウンロード、アップロード、管理するためのツールです。

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.