huggingface-papers 是做什么的？
查找并阅读 Hugging Face 论文页面的 Markdown 版本，并使用论文 API 获取结构化元数据，如作者、关联的模型/数据集/空间、GitHub 仓库和项目页面。当用户分享 Hugging Face 论文页面 URL、arXiv URL 或 ID，或要求总结、解释或分析 AI 研究论文时使用。
查找并阅读 Hugging Face 论文页面的 Markdown 版本，并使用论文 API 获取结构化元数据，如作者、关联的模型/数据集/空间、GitHub 仓库和项目页面。当用户分享 Hugging Face 论文页面 URL、arXiv URL 或 ID，或要求总结、解释或分析 AI 研究论文时使用。
huggingface/huggingface-papers 是一个 AI 代理技能，旨在帮助开发者高效查找、阅读和分析 AI 研究论文。该技能支持从 Hugging Face 论文页面和 arXiv 获取论文的 Markdown 格式内容，并利用 Hugging Face 的 Papers API 提取丰富的结构化元数据，包括作者列表、关联的模型、数据集和 Spaces 资源、GitHub 仓库链接以及项目页面。这对于需要快速理解论文核心内容、追踪相关资源或进行文献综述的研究者非常有用。该技能适用于 Claude Code、Cursor 和 Codex 等 AI 代理平台，通过简单的 URL 或 ID 输入即可触发。其工作流程是：用户提供论文来源，技能自动获取 Markdown 内容并调用 API 获取元数据，最终以清晰的结构化信息呈现给用户，从而简化了从论文阅读到资源发现的过程。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-papersInstalled? 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 →
查找并阅读 Hugging Face 论文页面的 Markdown 版本，并使用论文 API 获取结构化元数据，如作者、关联的模型/数据集/空间、GitHub 仓库和项目页面。当用户分享 Hugging Face 论文页面 URL、arXiv URL 或 ID，或要求总结、解释或分析 AI 研究论文时使用。
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.