Consultez et lisez les pages de documents Hugging Face en markdown, et utilisez l'API papers pour les métadonnées structurées (auteurs, modèles/datasets/spaces liés, repo GitHub et page projet). Utilisez-le lorsque l'utilisateur partage une URL de page de document Hugging Face, une URL ou un ID arXiv, ou demande de résumer, expliquer ou analyser un article de recherche en IA.
Cette compétence d'agent spécialisée, 'huggingface/huggingface-papers', permet aux agents IA (comme Claude Code, Cursor, Codex) d'interagir avec la plateforme Hugging Face Papers et les articles arXiv via l'API dédiée. Elle permet de consulter les pages de documents au format Markdown, ainsi que d'extraire des métadonnées structurées détaillées : auteurs, modèles associés, ensembles de données (datasets), espaces (Spaces), dépôts GitHub et pages de projet. L'agent peut ainsi résumer, analyser ou expliquer des articles de recherche en IA sur simple partage d'une URL Hugging Face, arXiv ou d'un ID arXiv. Cette compétence est particulièrement utile pour les développeurs et chercheurs qui souhaitent intégrer rapidement des informations actualisées sur les publications scientifiques dans leurs workflows, en automatisant la récupération et le traitement des données liées à la recherche.
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-papersInstalled? Explore more Recherche et analyse de données skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Consultez et lisez les pages de documents Hugging Face en markdown, et utilisez l'API papers pour les métadonnées structurées telles que les auteurs, les modèles/datasets/spaces liés, le dépôt GitHub et la page projet. Utilisez-le lorsque l'utilisateur partage une URL de page de document Hugging Face, une URL ou un ID arXiv, ou demande de résumer, expliquer ou analyser un article de recherche en IA.
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.