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Hugging Face Trending

Curate trending Hugging Face models, datasets, and spaces with filtering and metadata.

Updated Jun 2, 2026
Search Tags:#curation#models#artifact#datasets#huggingface#trending#spaces
One-click install
npx skills add https://github.com/Atrium-Hermes/atrium-lighthouse --skill hugging-face-trending-atrium-hermes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Hugging Face Trending
Source: https://github.com/Atrium-Hermes/atrium-lighthouse/tree/main/skills/huggingface-trending
Command: npx skills add https://github.com/Atrium-Hermes/atrium-lighthouse --skill hugging-face-trending-atrium-hermes

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Curating the signal from Hugging Face artifacts today can be time-consuming; this skill surfaces top models, datasets, and spaces with clear context and rationale.

Core Features & Use Cases

  • Curates 4–8 trending artifacts across models, datasets, and spaces with a concise "why notable" line.
  • Groups artifacts into meaningful categories (LLMs / Reasoning, Multimodal, Datasets, Spaces) for quick scannability.
  • Maintains momentum signals (DEBUT / ACCELERATING / RETURNING) to highlight emerging shifts.

Quick Start

Generate a daily HF trending digest highlighting top models, datasets, and spaces with concise why-notable notes.

Frequently Asked Questions about Hugging Face Trending

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find trending Hugging Face models and datasets without manually browsing?▼

To find trending Hugging Face artifacts without manual browsing, this skill queries trending endpoints and applies filtering rules to surface 4–8 notable models, datasets, and spaces. It groups items into categories like LLMs, Multimodal, and Datasets for quick scanning.

What is the best way to track new Hugging Face spaces gaining momentum?▼

Tracking momentum for Hugging Face spaces is handled by applying momentum tags such as DEBUT, ACCELERATING, or RETURNING to curated items. This approach highlights emerging shifts in popularity by filtering trending endpoints for notable artifacts.

How do I generate a daily digest of top Hugging Face artifacts?▼

Generating a daily digest of top Hugging Face artifacts involves curating 4–8 trending items with concise why-notable lines. The skill produces structured metadata including id, likes, downloads, and pipeline_tag while excluding noisy items from the trending endpoints.

Can I filter Hugging Face trending datasets by pipeline tag and likes?▼

Filtering Hugging Face trending datasets by pipeline tag and likes is supported through the generation of structured metadata. The skill extracts specific metrics including id, likes, downloads, and pipeline_tag for each curated artifact to provide clear context.

Does this curation approach exclude noisy items from Hugging Face trending endpoints?▼

Excluding noisy items from Hugging Face trending endpoints is a core function of this curation approach. The skill applies filtering rules and logs decisions to ensure only high-signal artifacts across models, datasets, and spaces are surfaced in the final output.

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