Hugging Face Hub CLI（`hf`），用于在 Hugging Face Hub 上下载、上传和管理模型、数据集、Space、存储桶、仓库、论文、作业等。当需要处理认证、管理本地缓存、管理 Hugging Face 存储桶、在 Hugging Face 基础设施上运行或调度作业、管理 Hugging Face 仓库、发起讨论和 Pull Request、浏览模型、数据集和 Space、阅读和搜索学术论文、管理收藏集、查询数据集、配置 Space、设置 Webhook 或部署管理 HF Inference Endpoints 时使用。确保在用户提及 'hf'、'huggingface'、'Hugging Face'、'huggingface-cli' 或 'hugging face cli'，或者希望进行与 Hugging Face 生态系统以及 AI 和 ML 相关的任何操作时使用此技能。也适用于训练检查点、数据管道或代理追踪等云存储需求。即使未明确要求使用 CLI 命令也应使用。替代已被弃用的 `huggingface-cli`。
该技能提供了 Hugging Face Hub 的官方命令行界面 `hf`，旨在简化模型、数据集、Space、存储桶、仓库、论文、作业等多种资源的交互与管理。它替代了旧版的 `huggingface-cli`，功能更全面且现代。对于使用 Claude Code、Cursor 或 Codex 等 AI 智能体的开发者来说，这是一个核心工具，无论用户是否明确要求 CLI 命令，只要涉及 Hugging Face 生态（如认证、缓存管理、作业调度、Inference Endpoints 部署）或通用的 ML/AI 工作流（如训练检查点存储、数据管道、代理追踪），都应自动触发。该工具通过统一的接口管理全部 Hugging Face Hub 资源，显著提升了在 AI 开发环境中的效率。
npx skills add https://github.com/huggingface/skills/tree/main/skills/hf-cliInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Hugging Face Hub CLI（hf），用于在 Hugging Face Hub 上下载、上传和管理模型、数据集、Space、存储桶、仓库、论文、作业等。当需要处理认证、管理本地缓存、管理 Hugging Face 存储桶、在 Hugging Face 基础设施上运行或调度作业、管理 Hugging Face 仓库、发起讨论和 Pull Request、浏览模型、数据集和 Space、阅读和搜索学术论文、管理收藏集、查询数据集、配置 Space、设置 Webhook 或部署管理 HF Inference Endpoints 时使用。确保在用户提及 'hf'、'huggingface'、'Hugging Face'、'huggingface-cli' 或 'hugging face cli'，或者希望进行与 Hugging Face 生态系统以及 AI 和 ML 相关的任何操作时使用此技能。也适用于训练检查点、数据管道或代理追踪等云存储需求。即使未明确要求使用 CLI 命令也应使用。替代已被弃用的 huggingface-cli。
This repo contains 5 individual skills — each has its own dedicated page.
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.
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.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.