monitor-experiment

Name: monitor-experiment
Availability: InStock
Author: dogekiki

Monitor remote machine learning experiment status and performance metrics across SSH, Vast.ai, and Modal.

1|Updated Jul 21, 2026

Search Tags:#automation #monitoring #ssh #modal #experiment-tracking #weights-and-biases #vast-ai

Ready to run

npx skills add https://github.com/dogekiki/SP-test --skill monitor-experiment-dogekiki

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monitor-experiment
Source: https://github.com/dogekiki/SP-test/tree/main/.trae/skills/monitor-experiment
Command: npx skills add https://github.com/dogekiki/SP-test --skill monitor-experiment-dogekiki

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates the tedious process of checking experiment status, gathering logs, and parsing metrics across distributed infrastructure like SSH servers, Vast.ai, and Modal.

Core Features & Use Cases

Multi-Platform Monitoring: Seamlessly checks status across SSH, Vast.ai, and Modal environments.
Automated Metric Collection: Extracts training loss, evaluation metrics, and system logs directly from running sessions.
Result Synthesis: Generates comparative summary tables and provides W&B integration for deep-dive analysis.

Quick Start

Ask the assistant to monitor the current experiment status and summarize the latest results for the specified server.

Frequently Asked Questions about monitor-experiment

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

FAQPage Schema

How do I monitor remote machine learning experiments across SSH servers?▼

To monitor remote machine learning experiments across SSH servers, you can automate the collection of training logs and performance metrics. This skill checks execution status and parses JSON result files directly from running sessions.

Can I track training metrics from Vast.ai and Modal environments?▼

Yes, you can track training metrics from Vast.ai and Modal environments. The skill provides multi-platform monitoring to seamlessly check status, extract evaluation metrics, and gather system logs across these diverse cloud infrastructures.

How do I automate collection of Weights and Biases metrics from distributed infrastructure?▼

You can automate collection of Weights and Biases metrics by integrating with experiment tracking APIs. This facilitates validating job completion and synthesizing comparative summary tables for deep-dive analysis of your runs.

What is the best way to parse training loss and system logs from running sessions?▼

The best way to parse training loss and system logs from running sessions is through automated metric extraction. This skill automatically extracts these values directly from active environments and consolidates them into a performance summary.

Does this experiment tracking require SSH access to validate job completion?▼

Yes, this experiment tracking requires SSH access to validate job completion and resource utilization. It also requires integration with experiment tracking APIs to properly monitor remote infrastructure.

How do I generate comparative summary tables for machine learning experiment results?▼

To generate comparative summary tables for machine learning experiment results, the skill synthesizes collected logs and metrics. It extracts training loss and evaluation data to provide a consolidated performance overview for analysis.