monitor-experiment

Monitor running experiments across SSH servers, Vast.ai, and Modal apps.

Updated Jun 10, 2026

Search Tags:#automation #logs #ssh #experiments #monitor #results #wandb

Ready to run

npx skills add https://github.com/xqinag/ARIS-new --skill monitor-experiment-xqinag

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monitor-experiment
Source: https://github.com/xqinag/ARIS-new/tree/main/skills/monitor-experiment
Command: npx skills add https://github.com/xqinag/ARIS-new --skill monitor-experiment-xqinag

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Monitors running experiments and collects progress and results to reduce manual checking and waiting time.

Core Features & Use Cases

Live status checks: quickly view which experiments are running, completed, or failed across SSH servers, Vast.ai, and Modal.
Result aggregation: fetch latest logs and JSON outputs, and optionally pull Weights & Biases metrics for evaluation.
Use Case: Researchers can automate progress verification and collect final results for reporting without manual log scraping.

Quick Start

Ask the agent to check the status of all active experiments and fetch the latest results.

Frequently Asked Questions about monitor-experiment

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

FAQPage Schema

How do I monitor running experiments across remote servers?▼

You can monitor running experiments across SSH servers, Vast.ai instances, and Modal apps by checking live statuses and capturing logs to verify progress without manual intervention. The skill aggregates these checks into a structured summary.

What is the best way to collect experiment results and logs from Vast.ai?▼

Collecting experiment results from Vast.ai involves fetching latest logs, discovering JSON outputs, and enforcing deterministic checks like exit codes and epoch logging. This process aggregates final results for review without manual log scraping.

Can I pull Weights and Biases metrics when checking experiment status?▼

Yes, you can pull Weights and Biases metrics when checking experiment status if WandB is enabled. The skill supports optional WandB data retrieval to fetch evaluation metrics alongside standard log capture and JSON result discovery.

Does this experiment monitoring approach work with Modal apps?▼

Yes, this experiment monitoring approach works with Modal apps. It covers live status checks for running, completed, or failed apps, enforcing deterministic checks like file presence and exit codes to ensure accurate progress verification.

How do I verify if an experiment failed or completed on an SSH server?▼

To verify if an experiment failed or completed on an SSH server, the skill enforces deterministic checks using exit codes, file presence, and epoch logging. It outputs a structured summary detailing the live status of each experiment.

Why are deterministic checks important for experiment monitoring?▼

Deterministic checks are important for experiment monitoring because they rely on exit codes, file presence, and epoch logging to accurately verify progress. This prevents ambiguous status reporting and ensures reliable result aggregation across platforms.