A high-performance skill for AI agents to retrieve and summarize trending AI models and datasets from HF-Mirror into structured Chinese reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hfmirror-trending
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 hfmirror-trending 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 HF-Mirror Trending?
The HF-Mirror Trending skill is a specialized utility designed to give AI agents autonomous access to the heartbeat of the AI community. By leveraging the HF-Mirror public REST API, this skill allows agents to identify which models and datasets are currently gaining traction. It is an essential component of the Openclaw Skills ecosystem for developers building research-oriented or news-focused AI assistants.
This skill is built with a zero-dependency philosophy, ensuring it runs seamlessly across Windows, macOS, and Linux environments using only the Python standard library. It transforms raw JSON data into beautifully structured Chinese Markdown reports, making complex technical trends accessible to end-users instantly.
HF-Mirror Trending Use Cases
- Tracking the most popular Large Language Models (LLMs) and datasets daily.
- Providing real-time answers to user queries about what is hot on Hugging Face.
- Generating structured technical reports for AI researchers and developers.
- Monitoring specific AI domains such as ASR, TTS, and OCR through task labels.
How HF-Mirror Trending Works
- The AI agent detects a user intent related to trending models or Hugging Face updates.
- The agent executes the internal Python script with the fetch flag to initiate a network request.
- The script calls the public HF-Mirror API to retrieve the current trending JSON payload.
- Data is processed to normalize metrics, such as converting parameter counts into readable formats like 7B or 27B.
- A structured Markdown file is generated in Chinese, which the agent then presents to the user.
HF-Mirror Trending Setup
To integrate this into your environment, ensure you have Python 3.6 or higher installed. No third-party packages are required. Navigate to the skill directory and execute the following command:
python scripts/summarize.py --fetch trending_report.md
The script will automatically handle the API request and output the results to the specified Markdown file.
HF-Mirror Trending Data Schema & Taxonomy
The skill organizes fetched data into a clear taxonomy within the generated report:
| Attribute | Description |
|---|---|
| Model ID | The unique repository identifier on Hugging Face. |
| Downloads | Real-time download statistics showing community adoption. |
| Likes | Social validation count from the AI community. |
| Parameter Scale | Normalized parameter sizes (e.g., 7B, 14B) for deployment assessment. |
| Task Labels | Categorization by AI task, including ASR, TTS, and Image Generation. |
HF-Mirror Trending Advanced Features
- Zero-dependency architecture utilizing only built-in Python libraries like json and urllib.
- Cross-platform compatibility across all major operating systems and CLI environments.
- Automated parameter size conversion to help users evaluate hardware requirements.
- Compliant API access using a dedicated User-Agent for stable data retrieval.
- Dynamic report generation that bridges the gap between raw API data and user-friendly documentation.
SKILL.md
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