Hfmirror Trending En

Fetches real-time Hugging Face trending data via the public HF-Mirror API and generates structured Markdown reports in English. Suitable for conversational AI agents.

hfmirror_trending_en (Cross-platform Generic Version)

This Skill enables AI agents to autonomously fetch and parse real-time trending data from HF-Mirror (hf-mirror.com).

Data Source Notice: This Skill calls https://hf-mirror.com/api/trending — a public, login-free REST API provided by HF-Mirror. It does not require any tokens or authorization, nor does it involve any authenticated web scraping or bypassing of access controls.

Use Cases

When a user inquires about recent trending models, datasets, or projects on Hugging Face or its mirror. Examples:

• "What are the trending models lately?"
• "What's hot on Hugging Face right now?"
• "Push today's Hugging Face mirror trending list."
• "Help me parse the trending data from HF-Mirror."

Agent Workflow

When processing the above commands, AI agents should follow this standard end-to-end logic:

1. Auto-Fetch and Parse: The agent should call the processing script located in the Skill's root directory, utilizing its built-in networking capabilities.

   python scripts/summarize.py --fetch [out_path.md]
   

   Note: The script is Python 3 compatible and can be run directly in Windows (PowerShell/CMD), Linux (Shell), or macOS environments.

2. Generate Elegant Reports: The script automatically fetches JSON from https://hf-mirror.com/api/trending and generates structured Markdown output in English.

3. Smart Delivery: The agent reads the generated file content and presents it as a well-formatted message to the user.

Core Design (Cross-Platform & Environment Decoupled)

• Path Agnostic: Agents can locate scripts/summarize.py via relative paths or Skill environment configurations based on their current context.
• Zero Dependencies: The script relies solely on Python 3 standard libraries (json, urllib, os, sys). It requires no third-party packages, allowing it to run smoothly even in minimal container or CLI environments.
• Dynamic Fetch: The built-in --fetch argument eliminates the need to manually prepare intermediate files, enabling a seamless one-click transition from API to report.
• Compliant Access: Uses a named User-Agent (hfmirror-trending-en-skill/1.0) to identify the request source, adhering to public API best practices.

Core Output Fields Explanation

• Model ID: The unique identifier for the model.
• Downloads & Likes: Metrics reflecting community popularity.
• Parameter Size: Automatically converted (e.g., 7B, 27B) to help users evaluate deployment costs.
• Pipeline Tag: Distinction between different AI domains such as ASR, TTS, OCR, etc.