A specialized tool for discovering and tracking the most popular AI models on Hugging Face by task, framework, and engagement metrics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install huggingface-trends
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install huggingface-trends using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Hugging Face Trends?
The Hugging Face Trends skill is a technical utility designed for developers and researchers who need to stay ahead of the rapidly evolving AI landscape. By leveraging the Hugging Face API, this Openclaw Skills integration allows users to programmatically fetch, filter, and analyze the most popular models currently being released and used by the community. It provides a direct bridge between raw model metadata and actionable research, ensuring you never miss a significant release.
Whether you are looking for the latest large language models or niche computer vision architectures, this skill streamlines the discovery process directly from your terminal. It handles complex filtering and offers robust proxy configuration to ensure seamless access to the global AI ecosystem, even in restricted network environments. Using this within your Openclaw Skills setup ensures you are always working with the most relevant state-of-the-art models.
Hugging Face Trends Use Cases
- Daily monitoring of new AI model releases and rising stars in the machine learning community.
- Researching top-performing models for specific tasks like text-generation, image-classification, or summarization.
- Comparing the popularity and adoption rates between different machine learning frameworks like PyTorch, TensorFlow, and JAX.
- Automating the collection of model metadata for competitive analysis within an Openclaw Skills workflow.
- Exporting trending model data to JSON for custom dashboarding or reporting.
How Hugging Face Trends Works
- The skill initializes a connection to the Hugging Face API, utilizing a configured HTTP proxy if necessary to bypass specific network restrictions.
- It sends a request to fetch model metadata based on user-defined parameters such as result limits and sort order (trending, likes, or downloads).
- The tool applies granular filters for specific AI tasks (e.g., translation) or ML libraries (e.g., transformers) to narrow down the results.
- It processes the raw API response to extract critical metrics including like counts, download frequency, and creation dates.
- The skill generates a formatted terminal output for immediate human review or exports a structured JSON file for further programmatic use.
Hugging Face Trends Setup
To integrate this into your environment, ensure you have the necessary dependencies and configuration:
# Install the required Python dependencies
pip install requests
# Basic usage to fetch the top 10 trending models via proxy
python scripts/hf_trends.py -n 10 -p http://172.28.96.1:10808
# Fetch models for a specific task
python scripts/hf_trends.py -t text-generation -n 5 -p http://172.28.96.1:10808
Hugging Face Trends Data Schema & Taxonomy
The skill organizes fetched data into a structured format. When using the JSON export feature, the following fields are captured for every model:
| Field | Description |
|---|---|
| Model ID | The full name/path of the model on Hugging Face |
| Likes | Total number of community likes (popularity) |
| Downloads | Aggregate download count |
| Task | The primary AI pipeline/task assigned to the model |
| Library | The compatible ML framework (e.g., pytorch, jax) |
| Created/Updated | Timestamps for model lifecycle tracking |
Hugging Face Trends Advanced Features
- Customizable HTTP proxy routing to support Openclaw Skills in enterprise or restricted network environments.
- Multi-metric sorting capabilities, allowing users to pivot between absolute popularity (likes) and recent velocity (trending).
- Programmatic Python API which allows for the embedding of trending model discovery directly into automated AI agent pipelines.
- Headless JSON export mode for seamless integration with external data analysis tools and CI/CD triggers.
SKILL.md
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