← Back to Skills Marketplace
gpu-cluster-monitor
by
SounderLiu
· GitHub ↗
· v1.0.2
558
Downloads
0
Stars
0
Active Installs
3
Versions
Install in OpenClaw
/install gpu-cluster-monitor
Description
Monitors GPU cluster health and usage, providing real-time status, performance metrics, and alerts for efficient resource management.
README (SKILL.md)
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.
Usage Guidance
Do not install this expecting a GPU cluster monitor — the skill is actually a containerized deep web scraper. Before proceeding: 1) Verify the author's identity and source (homepage is missing). 2) Ask for the Dockerfile and review it; do not build/run the container until you inspect its contents. 3) Run any testing inside an isolated VM or sandbox with no sensitive mounts and restricted network access. 4) Be aware the code intercepts page network requests and performs UI automation; it can capture API responses or tokens if pointed at authenticated pages. 5) If you wanted GPU monitoring, reject this package and look for a different, clearly named skill that uses nvidia-smi / Prometheus exporters and requests only the credentials it needs. 6) If you must use this scraper, ensure it complies with target sites' terms of service and applicable law, and avoid running it with elevated privileges or mounting host directories containing secrets.
Capability Analysis
Type: OpenClaw Skill
Name: gpu-cluster-monitor
Version: 1.0.2
The skill is classified as suspicious due to inherent security risks associated with its execution model and powerful capabilities, rather than explicit malicious intent. It utilizes Docker to run Playwright for 'deep web scraping' and 'penetrating protections,' which involves executing a browser with `--no-sandbox` and `--disable-setuid-sandbox` flags (in `assets/main_handler.js`), a known vulnerability risk for browser exploits. Furthermore, the `SKILL.md` instructs the agent to execute a `docker run` command with a user-provided `[TARGET_URL]`, which, if not properly sanitized by the OpenClaw agent, could lead to shell injection on the host machine. While the skill's code itself does not demonstrate malicious actions like data exfiltration or persistence, these vulnerabilities and the powerful nature of the scraping tool warrant a 'suspicious' classification.
Capability Assessment
Purpose & Capability
Name and description claim a GPU cluster monitor, but SKILL.md and the included code implement a 'deep-scraper' (YouTube/X scraping with network interception). A GPU monitoring skill would not need Crawlee/Playwright or instructions for building a Docker scraper. This is a major mismatch.
Instruction Scope
The SKILL.md instructs building/running a containerized Playwright/Crawlee scraper that intercepts network requests, clears cookies, and triggers UI interactions to capture hidden APIs/transcripts. That scope goes beyond a resource monitor and includes actions that could capture sensitive network responses or personally identifiable content; the instructions also claim 'penetrate protections' which is concerning.
Install Mechanism
There is no formal install spec, but SKILL.md expects building a Docker image (clawd-crawlee). The manifest does not include a Dockerfile despite instructing the user to keep one in the skill directory. package.json declares crawlee/playwright dependencies and an openclaw docker requirement. Missing Dockerfile and mismatch between registry metadata and instructions is an inconsistency the user should verify.
Credentials
The skill requests no environment variables, but requires Docker (privileged capability to run containers) and network access; the scraping code listens to all page network requests and can fetch intercepted URLs. That capability is not justified by the registry name/description and could capture tokens or private API responses if misused.
Persistence & Privilege
always is false and the skill doesn't request system-wide config changes. However, it requires the ability to run Docker containers which grants substantial runtime privileges on the host; run-time container privilege should be considered when evaluating risk.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install gpu-cluster-monitor - After installation, invoke the skill by name or use
/gpu-cluster-monitor - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
- Version bump to 1.0.2 with no functional or documentation changes detected.
- No file modifications; the skill remains unchanged.
v1.0.1
- Initial release of gpu-cluster-monitor skill (version 1.0.0)
- No file changes detected for this version.
v1.0.0
Initial release of deep-scraper: a high-performance web scraping tool.
- Provides deep web scraping via containerized Docker + Crawlee (Playwright) environment.
- Bypasses protections on complex sites like YouTube and X/Twitter to extract raw data.
- Outputs data as structured JSON for easy processing.
- Enforces strict privacy: avoids password-protected and private data.
- Auto-strips ads and non-essential content for clean LLM-ready output.
Metadata
Frequently Asked Questions
What is gpu-cluster-monitor?
Monitors GPU cluster health and usage, providing real-time status, performance metrics, and alerts for efficient resource management. It is an AI Agent Skill for Claude Code / OpenClaw, with 558 downloads so far.
How do I install gpu-cluster-monitor?
Run "/install gpu-cluster-monitor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is gpu-cluster-monitor free?
Yes, gpu-cluster-monitor is completely free (open-source). You can download, install and use it at no cost.
Which platforms does gpu-cluster-monitor support?
gpu-cluster-monitor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created gpu-cluster-monitor?
It is built and maintained by SounderLiu (@sounderliu); the current version is v1.0.2.
More Skills