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gpu-cluster-monitor

Monitors GPU cluster health and usage, providing real-time status, performance metrics, and alerts for efficient resource management.

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版本1.0.2
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技能说明

Skill: deep-scraper

Overview

A high-performance engineering tool for deep web scraping. It uses a containerized Docker + Crawlee (Playwright) environment to penetrate protections on complex websites like YouTube and X/Twitter, providing "interception-level" raw data.

Requirements

  1. Docker: Must be installed and running on the host machine.
  2. Image: Build the environment with the tag clawd-crawlee.
    • Build command: docker build -t clawd-crawlee skills/deep-scraper/

Integration Guide

Simply copy the skills/deep-scraper directory into your skills/ folder. Ensure the Dockerfile remains within the skill directory for self-contained deployment.

Standard Interface (CLI)

docker run -t --rm -v $(pwd)/skills/deep-scraper/assets:/usr/src/app/assets clawd-crawlee node assets/main_handler.js [TARGET_URL]

Output Specification (JSON)

The scraping results are printed to stdout as a JSON string:

  • status: SUCCESS | PARTIAL | ERROR
  • type: TRANSCRIPT | DESCRIPTION | GENERIC
  • videoId: (For YouTube) The validated Video ID.
  • data: The core text content or transcript.

Core Rules

  1. ID Validation: All YouTube tasks MUST verify the Video ID to prevent cache contamination.
  2. Privacy: Strictly forbidden from scraping password-protected or non-public personal information.
  3. Alpha-Focused: Automatically strips ads and noise, delivering pure data optimized for LLM processing.

如何使用「gpu-cluster-monitor」?

  1. 打开小龙虾AI(Web 或 iOS App)
  2. 点击上方「立即使用」按钮,或在对话框中输入任务描述
  3. 小龙虾AI 会自动匹配并调用「gpu-cluster-monitor」技能完成任务
  4. 结果即时呈现,支持继续对话优化

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