monitor-experiment

Monitor running experiments and aggregate logs, JSON outputs, and summaries.

Updated Apr 18, 2026

Search Tags:#automation #ssh #progress #experiments #monitor #results

Ready to run

npx skills add https://github.com/THUFanZd/Sewed_pipeline --skill monitor-experiment-thufanzd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: monitor-experiment
Source: https://github.com/THUFanZd/Sewed_pipeline/tree/main/.agents/skills/monitor-experiment
Command: npx skills add https://github.com/THUFanZd/Sewed_pipeline --skill monitor-experiment-thufanzd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Monitor and orchestrate ongoing experiments, collecting progress updates and final results to keep teams informed and aligned.

Core Features & Use Cases

Live progress tracking: Continuously monitor running experiments and surface status changes.
Result aggregation: Gather logs, JSON outputs, and summaries into a single view.
Use Case: A data science team runs multiple hyperparameter sweeps and wants a unified view of which experiments completed and their key metrics.

Quick Start

Run the monitor-experiment workflow to start watching a set of experiments and surface the latest status.

Frequently Asked Questions about monitor-experiment

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

FAQPage Schema

How do I monitor experiment progress and results across remote servers?▼

You can monitor experiment progress across remote servers by automating status checks and output collection via SSH. This process continuously tracks running jobs, identifies completion status, and aggregates final summaries into a unified view.

How do I aggregate experiment logs and outputs from multiple containers?▼

Aggregating experiment logs and JSON outputs from multiple containers is done by automating result collection. The workflow gathers logs, metrics, and summaries from local or remote jobs, surfacing the latest status and final outputs in a single view.

Can I track hyperparameter sweep status and key metrics in real time?▼

Tracking hyperparameter sweep status and key metrics in real time is supported by live progress monitoring. It continuously watches running experiments, identifies which jobs have completed, and aggregates their final results for a unified team view.

Does automated experiment monitoring work with jobs running on local clusters?▼

Automated experiment monitoring works with jobs running on local clusters, remote servers, or containers. It applies to experimental jobs across these environments, checking for progress, completion status, and output aggregation without requiring additional dependencies.

What is the best way to collect last outputs from running experiments?▼

The best way to collect last outputs from running experiments is to automate result aggregation. This approach continuously checks progress, identifies running status, and reports final summaries, gathering logs and JSON outputs into a single view.

Can I receive notifications when an experimental job completes?▼

You can receive notifications when an experimental job completes through optional notification steps. The monitoring workflow automates status checks, aggregates final outputs, and can trigger these notification steps when it identifies that a job has finished running.