Busca y lee páginas de papers de Hugging Face en markdown, y utiliza la API de papers para obtener metadatos estructurados como autores, modelos/datasets/spaces vinculados, repositorio de GitHub y página del proyecto. Úsalo cuando el usuario comparta una URL de un paper de Hugging Face, una URL o ID de arXiv, o pida resumir, explicar o analizar un artículo de investigación en IA.
Esta habilidad permite a un agente de IA buscar, leer y analizar páginas de papers de Hugging Face (huggingface.co/papers) obteniendo su contenido en formato markdown. También utiliza la API de papers de Hugging Face para recuperar metadatos estructurados, como autores, modelos, conjuntos de datos y spaces vinculados, así como el repositorio de GitHub y la página del proyecto asociados. Es útil cuando un usuario comparte una URL de un paper de Hugging Face, una URL o ID de arXiv, o cuando pide resumir, explicar o analizar un artículo de investigación en inteligencia artificial. Funciona en plataformas compatibles con agentes de código como Claude Code, Cursor y Codex, y se activa ante comandos como "resumen de este paper" o "explícame este artículo de arXiv".
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-papersInstalled? Explore more Investigación y análisis de datos skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Busca y lee páginas de papers de Hugging Face en markdown, y utiliza la API de papers para obtener metadatos estructurados como autores, modelos/datasets/spaces vinculados, repositorio de GitHub y página del proyecto. Úsalo cuando el usuario comparta una URL de un paper de Hugging Face, una URL o ID de arXiv, o pida resumir, explicar o analizar un artículo de investigación 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.