huggingface-papers 是做什麼的？
查閱並閱讀 Hugging Face 論文頁面的 Markdown 格式內容，並使用論文 API 取得結構化後設資料，例如作者、相關模型/資料集/空間、GitHub 儲存庫與專案頁面。當使用者分享 Hugging Face 論文頁面網址、arXiv 網址或 ID，或要求摘要、解釋、分析某篇 AI 研究論文時使用。
查閱並閱讀 Hugging Face 論文頁面的 Markdown 格式內容，並使用論文 API 取得結構化後設資料，例如作者、相關模型/資料集/空間、GitHub 儲存庫與專案頁面。當使用者分享 Hugging Face 論文頁面網址、arXiv 網址或 ID，或要求摘要、解釋、分析某篇 AI 研究論文時使用。
這個技能讓 AI 助手能夠直接與 Hugging Face 的論文平台互動。它可以將 Hugging Face 論文頁面轉換為 Markdown 格式進行閱讀，並透過論文 API 取得結構化的後設資料，包括作者列表、相關的模型、資料集、Spaces、GitHub 儲存庫以及專案頁面。主要支援 Hugging Face 論文頁面網址、arXiv 網址或 ID 作為輸入。對於開發者與研究人員而言，此技能在需要快速摘要、解釋或分析 AI 研究論文時非常實用，能夠從結構化資料中提取關鍵資訊，無需手動瀏覽多個頁面。它適用於任何整合了此技能的 AI 代理平台，例如 Claude Code、Cursor 或 Codex，透過簡單的指令即可取得論文內容與後設資料，大幅提升研究效率。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-papersInstalled? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
查閱並閱讀 Hugging Face 論文頁面的 Markdown 格式內容，並使用論文 API 取得結構化後設資料，例如作者、相關模型/資料集/空間、GitHub 儲存庫與專案頁面。當使用者分享 Hugging Face 論文頁面網址、arXiv 網址或 ID，或要求摘要、解釋、分析某篇 AI 研究論文時使用。
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.
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.
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.