huggingface-datasets 是做什麼的？
使用此技能處理 Hugging Face Datasets Viewer API 工作流程，包含獲取子集/分割元數據、分頁行、搜尋文字、應用過濾器、下載 Parquet URL 以及讀取大小或統計資料。
使用此技能處理 Hugging Face Datasets Viewer API 工作流程，包含獲取子集/分割元數據、分頁行、搜尋文字、應用過濾器、下載 Parquet URL 以及讀取大小或統計資料。
此技能專為 Hugging Face Datasets Viewer API 設計，使 AI 代理能高效地與資料集互動。它支援擷取子集與分割的元數據，並能以分頁方式瀏覽資料行，讓使用者能逐步探索大型資料集。此外，技能內建文字搜尋與過濾功能，能精確定位特定資料點，並提供下載 Parquet 格式檔案的功能，便於離線分析。技能亦能讀取資料集的大小與統計資訊，幫助開發者快速瞭解資料集全貌。此技能可在多種 AI 代理平台（如 Claude Code、Cursor 或 Codex）上執行，基於 Hugging Face 公開 API 建構，確保流程穩定可靠。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-datasetsInstalled? Explore more 生產力與協作 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
使用此技能處理 Hugging Face Datasets Viewer API 工作流程，包含獲取子集/分割元數據、分頁行、搜尋文字、應用過濾器、下載 Parquet URL 以及讀取大小或統計資料。
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`.
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
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.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.