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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.
安全通过
💬Prompt
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
- Docker: Must be installed and running on the host machine.
- Image: Build the environment with the tag
clawd-crawlee.- Build command:
docker build -t clawd-crawlee skills/deep-scraper/
- Build command:
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 | ERRORtype: TRANSCRIPT | DESCRIPTION | GENERICvideoId: (For YouTube) The validated Video ID.data: The core text content or transcript.
Core Rules
- ID Validation: All YouTube tasks MUST verify the Video ID to prevent cache contamination.
- Privacy: Strictly forbidden from scraping password-protected or non-public personal information.
- Alpha-Focused: Automatically strips ads and noise, delivering pure data optimized for LLM processing.
如何使用「gpu-cluster-monitor」?
- 打开小龙虾AI(Web 或 iOS App)
- 点击上方「立即使用」按钮,或在对话框中输入任务描述
- 小龙虾AI 会自动匹配并调用「gpu-cluster-monitor」技能完成任务
- 结果即时呈现,支持继续对话优化