huggingface-gradio は何をしますか？
Gradio の Web UI やデモを Python で構築します。Gradio アプリ、コンポーネント、イベントリスナー、レイアウト、チャットボットの作成や編集時に使用します。
Gradio の Web UI やデモを Python で構築します。Gradio アプリ、コンポーネント、イベントリスナー、レイアウト、チャットボットの作成や編集時に使用します。
このスキルは、huggingface/huggingface-gradio リポジトリに基づいており、Gradio を使用した Web UI やデモの Python での構築に特化しています。Gradio は、機械学習モデルやデータパイプライン用のインタラクティブなインターフェースを素早く作成できるフレームワークです。このスキルは、Gradio アプリ、コンポーネント（スライダー、テキストボックスなど）、イベントリスナー（クリック、変更など）、レイアウト（行、列、タブ）、チャットボットの作成や編集をサポートします。Hugging Face Spaces やローカル環境で動作し、開発者がコード生成中に適切な Gradio API を使用するためのガイダンスを提供します。主な利点は、Gradio の冗長な API 知識を必要とせずに、アシスタントが自然言語の指示から高品質な Gradio UI コードを生成または編集できることです。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-gradioInstalled? Explore more コーディング＆開発 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
Gradio の Web UI やデモを Python で構築します。Gradio アプリ、コンポーネント、イベントリスナー、レイアウト、チャットボットの作成や編集時に使用します。
This repo contains 5 individual skills — each has its own dedicated page.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
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.
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
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 browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.