huggingface-datasets 是做什么的？
使用此技能处理 Hugging Face 数据集查看器 API 工作流，包括获取子集/分割元数据、分页行数据、搜索文本、应用过滤器、下载 Parquet URL 以及读取大小或统计信息。
使用此技能处理 Hugging Face 数据集查看器 API 工作流，包括获取子集/分割元数据、分页行数据、搜索文本、应用过滤器、下载 Parquet URL 以及读取大小或统计信息。
此技能专门用于通过 Hugging Face 数据集查看器 API 高效地与数据集交互。它支持获取数据集的子集和分割元数据，实现行数据的分页浏览，执行文本搜索，应用过滤器，以及下载 Parquet 格式的数据文件 URL。此外，还能读取数据集的大小和统计信息，帮助开发者快速了解数据集概况。该技能适用于 Hugging Face 生态中的 AI 代理工具（如 Claude Code、Coder 等），尤其适合数据处理、模型训练准备和数据集探索场景。其核心价值在于将繁琐的 API 调用封装为简洁的工作流，节省开发者手动构造请求的时间，并确保数据获取的准确性和可重复性。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-datasetsInstalled? Explore more 生产力与协作 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
使用此技能处理 Hugging Face 数据集查看器 API 工作流，包括获取子集/分割元数据、分页行数据、搜索文本、应用过滤器、下载 Parquet URL 以及读取大小或统计信息。
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.