huggingface-papers은(는) 무엇을 하나요?
Hugging Face 논문 페이지를 마크다운으로 조회하고, 논문 API를 통해 저자, 연결된 모델/데이터셋/스페이스, GitHub 저장소 및 프로젝트 페이지와 같은 구조화된 메타데이터를 사용합니다. 사용자가 Hugging Face 논문 페이지 URL, arXiv URL 또는 ID를 공유하거나 AI 연구 논문을 요약, 설명, 분석해 달라고 요청할 때 사용하세요.
Hugging Face 논문 페이지를 마크다운으로 조회하고, 저자, 연결된 모델/데이터셋/스페이스, GitHub 저장소 등의 구조화된 메타데이터를 API로 제공합니다. 사용자가 Hugging Face 논문 URL, arXiv URL 또는 ID를 공유하거나 AI 연구 논문 요약/설명/분석을 요청할 때 사용하세요.
이 스킬은 AI 에이전트(Claude Code, Cursor 등)가 Hugging Face Papers API를 통해 논문 정보를 효율적으로 검색하고 활용할 수 있게 해줍니다. 사용자가 Hugging Face 논문 페이지 URL, arXiv URL 또는 arXiv ID를 제공하거나 특정 논문에 대한 요약, 설명, 분석을 요청하면, 스킬은 먼저 논문 페이지를 마크다운 형식으로 가져와 읽을 수 있습니다. 또한 Papers API를 통해 저자, 연결된 모델/데이터셋/스페이스, GitHub 저장소, 프로젝트 페이지 등의 구조화된 메타데이터를 제공하여 논문 관련 리소스에 대한 추가 탐색을 지원합니다. 이는 AI 연구 논문을 빠르게 이해하고 관련 자료를 연결하여 더 깊이 있는 분석을 가능하게 합니다.
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-papersInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Hugging Face 논문 페이지를 마크다운으로 조회하고, 논문 API를 통해 저자, 연결된 모델/데이터셋/스페이스, GitHub 저장소 및 프로젝트 페이지와 같은 구조화된 메타데이터를 사용합니다. 사용자가 Hugging Face 논문 페이지 URL, arXiv URL 또는 ID를 공유하거나 AI 연구 논문을 요약, 설명, 분석해 달라고 요청할 때 사용하세요.
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.