huggingface-papers は何をしますか？
Hugging Faceの論文ページをMarkdownで検索・閲覧し、著者や関連モデル/データセット/Spaces、Githubリポジトリ、プロジェクトページなどの構造化メタデータをPapers APIで取得します。ユーザーがHugging Faceの論文ページURL、arXivのURLまたはIDを共有した場合、あるいはAI研究論文の要約・説明・分析を依頼された場合に使用してください。
Hugging Faceの論文ページをMarkdownで検索・閲覧し、著者や関連モデル・データセット・Githubリポジトリなどの構造化メタデータをPapers APIで取得します。ユーザーがHugging Faceの論文URL、arXivのURL/IDを共有した場合や、AI研究論文の要約・説明・分析を依頼された場合に使用します。
このスキルは、Hugging Faceの論文ページ（papers page）をMarkdown形式で検索・閲覧し、Papers APIを介して構造化されたメタデータ（著者、関連モデル、データセット、Spaces、Githubリポジトリ、プロジェクトページなど）を取得する機能を提供します。特に、ユーザーがHugging Faceの論文URL、arXivのURLまたはIDを共有した場合、あるいはAI研究論文の要約・説明・分析を依頼した場合に有効です。対応プラットフォームは主にHugging FaceとarXivの論文データであり、API経由で効率的に情報を取得できるため、最新の研究動向を素早く把握したり、論文に関連するリソースを横断的に調査する際に有用です。スキルはMarkdown文書として返されるため、可読性が高く、他のツールと連携しやすい特長があります。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-papersInstalled? Explore more 研究＆データ分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Hugging Faceの論文ページをMarkdownで検索・閲覧し、著者や関連モデル/データセット/Spaces、Githubリポジトリ、プロジェクトページなどの構造化メタデータをPapers APIで取得します。ユーザーがHugging Faceの論文ページURL、arXivのURLまたはIDを共有した場合、あるいはAI研究論文の要約・説明・分析を依頼された場合に使用してください。
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.