monitor-experiment

Monitor remote machine learning experiments via SSH and collect JSON results.

14.4k|1.3k|Updated Mar 10, 2026

Search Tags:#mlops #reporting #progress tracking #ssh #results analysis #experiment monitoring

Ready to run

npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill monitor-experiment-wanshuiyin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monitor-experiment
Source: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/monitor-experiment
Command: npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill monitor-experiment-wanshuiyin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of monitoring ongoing machine learning experiments, collecting their results, and providing an interpretation of their progress and outcomes.

Core Features & Use Cases

Real-time Monitoring: Checks the status of experiments running on remote servers via SSH and screen sessions.
Result Collection: Fetches output logs and structured JSON results from experiments.
Interpretation & Reporting: Summarizes key metrics, compares against baselines, flags anomalies, and can send notifications.
Use Case: After initiating a long-running training job, you can ask the AI to "check results for my latest experiment" and receive a concise summary of its performance and status.

Quick Start

Use the monitor-experiment skill to check the progress of the experiment named 'resnet50-training'.

Frequently Asked Questions about monitor-experiment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I track remote machine learning experiment progress via SSH?▼

You can track remote machine learning experiment progress via SSH by checking running processes, fetching log files, and retrieving structured JSON outputs from your remote server sessions.

Can I monitor training results running in screen sessions on a remote server?▼

Yes, you can monitor training results running in screen sessions on a remote server. The system connects via SSH to check running processes and collect output logs from those active screen sessions.

How do I interpret model performance metrics against baselines after training?▼

To interpret model performance metrics against baselines, the system retrieves structured JSON results and output logs, then summarizes key metrics and flags anomalies to evaluate your training outcomes.

Does experiment monitoring support sending Feishu notifications for completion status?▼

Yes, experiment monitoring supports sending Feishu notifications. It can automatically push experiment completion status and summarized performance results directly to your Feishu messaging channels.

What is the best way to collect and analyze MLOps results from remote jobs?▼

The best way to collect and analyze MLOps results from remote jobs is to automate fetching output logs and structured JSON files via SSH, then interpret the performance metrics against your predefined baselines.