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huggingface/hf-cli

Hugging Face Hub CLI (`hf`) 用於在 Hugging Face Hub 上下載、上傳及管理模型、資料集、Space、儲存桶、儲存庫、論文、任務等。

hf-cli 是什麼？

此技能提供 Hugging Face Hub CLI (`hf`) 的完整功能，適用於 Hugging Face Hub 上的多種操作。其設計旨在取代已棄用的 `huggingface-cli`，並涵蓋更廣泛的使用情境。主要用途包括：處理身份驗證、管理本地快取與儲存桶、執行或排程任務、管理儲存庫與討論區、瀏覽模型、資料集與 Space、搜尋學術論文、管理收藏集、查詢資料集、配置 Space 與 Webhook，以及部署和管理 HF Inference Endpoints。當使用者提及 'hf'、'huggingface' 或任何與 Hugging Face 生態系、AI 及機器學習相關的內容時，建議優先使用此技能。此外，它也能滿足雲端儲存需求，例如訓練檢查點、資料管道或代理追蹤。即便使用者未明確要求 CLI 指令，此技能仍可主動介入。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/huggingface/skills/tree/main/skills/hf-cli

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說明文件

hf-cli 是做什麼的？

Hugging Face Hub CLI (hf) 用於在 Hugging Face Hub 上下載、上傳及管理模型、資料集、Space、儲存桶、儲存庫、論文、任務等。使用時機：處理身份驗證；管理本地快取；管理 Hugging Face 儲存桶；在 Hugging Face 基礎架構上執行或排程任務；管理 Hugging Face 儲存庫；討論區與 Pull Request；瀏覽模型、資料集與 Space；閱讀、搜尋或瀏覽學術論文；管理收藏集；查詢資料集；配置 Space；設定 Webhook；或部署及管理 HF Inference Endpoints。每當使用者提及 'hf'、'huggingface'、'Hugging Face'、'huggingface-cli' 或 'hugging face cli'，或希望執行任何與 Hugging Face 生態系、AI 及機器學習相關的操作時，請務必使用此技能。它也可用於雲端儲存需求，例如訓練檢查點、資料管道或代理追蹤。即使使用者未明確要求 CLI 指令，也請使用此技能。取代已棄用的 huggingface-cli。

Individual skills in this repo

This repo contains 5 individual skills — each has its own dedicated page.

huggingface/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.

huggingface/huggingface-gradio

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

huggingface/huggingface-llm-trainer

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.

huggingface/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. 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.

huggingface/transformers-js

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.

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