Diese Fähigkeit dient Hugging Face Dataset Viewer API-Workflows, die Subset-/Split-Metadaten abrufen, Zeilen paginieren, Text durchsuchen, Filter anwenden, Parquet-URLs herunterladen und Größen- oder Statistikdaten lesen.
Diese Fähigkeit ist speziell für Entwickler und KI-Agenten konzipiert, die mit der Hugging Face Dataset Viewer API arbeiten. Sie ermöglicht es, Metadaten zu Subsets und Splits von Datensätzen abzurufen, um einen Überblick über die verfügbaren Daten zu erhalten. Des Weiteren unterstützt sie das Paginieren von Datenzeilen, um große Datensätze effizient zu durchlaufen. Für die gezielte Datenextraktion bietet die Fähigkeit Funktionen zur Textsuche und Filterung. Zudem können Parquet-Datei-URLs heruntergeladen und Größen- oder Statistikinformationen (z.B. Anzahl der Zeilen, Spaltentypen) gelesen werden. Die Fähigkeit ist plattformunabhängig einsetzbar, sofern die Hugging Face API zugänglich ist, und vereinfacht wiederkehrende Aufgaben bei der Datensatzexploration und Vorverarbeitung.
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-datasetsInstalled? Explore more Produktivität & Zusammenarbeit skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
Verwenden Sie diese Fähigkeit für Hugging Face Dataset Viewer API-Workflows, die Subset-/Split-Metadaten abrufen, Zeilen paginieren, Text durchsuchen, Filter anwenden, Parquet-URLs herunterladen und Größen- oder Statistikdaten lesen.
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.