Utiliza esta habilidad para flujos de trabajo de la API de Visualización de Conjuntos de Datos de Hugging Face que obtienen metadatos de subconjuntos/divisiones, paginan filas, buscan texto, aplican filtros, descargan URLs de Parquet y leen tamaño o estadísticas.
Esta habilidad permite a los agentes de IA interactuar con la API de Visualización de Conjuntos de Datos de Hugging Face (Dataset Viewer API) para explorar y extraer información de conjuntos de datos públicos. Es útil cuando se necesita obtener metadatos de subconjuntos y divisiones, navegar por filas con paginación, realizar búsquedas de texto, aplicar filtros, descargar datos en formato Parquet y consultar estadísticas o tamaños. Está diseñada para su uso en plataformas como Claude Code, Cursor y Codex, facilitando la integración de datos de Hugging Face en tareas de machine learning y análisis. El agente utiliza endpoints REST para acceder a los datos estructurados, manejando la paginación y la conversión de respuestas JSON automáticamente.
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Utiliza esta habilidad para flujos de trabajo de la API de Visualización de Conjuntos de Datos de Hugging Face que obtienen metadatos de subconjuntos/divisiones, paginan filas, buscan texto, aplican filtros, descargan URLs de Parquet y leen tamaño o estadísticas.
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`.
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.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
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.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.