monitor-cron-sweep

Consolidate squeue and sacct job statuses into a unified cross-cluster report.

276|37|Updated Dec 5, 2025
One-click install
npx skills add https://github.com/open-thoughts/OpenThoughts-Agent --skill monitor-cron-sweep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: monitor-cron-sweep
Source: https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.claude/skills/monitor-cron-sweep
Command: npx skills add https://github.com/open-thoughts/OpenThoughts-Agent --skill monitor-cron-sweep

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Consolidate and summarize cluster job statuses across multiple clusters by collecting squeue and sacct data into a single, unified report.

Core Features & Use Cases

  • Cross-cluster aggregation: gather running and terminal state data, bucket jobs by type (RL, SFT, datagen, eval, catch-all), and render a single consolidated table for all clusters.
  • Health and action signals: identify silent hangs, inode headroom issues, and per-type red flags; propose the appropriate cleanup or investigation steps.
  • Deterministic outputs: produce a standardized job_monitor_table.md across clusters to support tracking and auditing of ongoing experiments.

Quick Start

Run the monitor-cron-sweep to generate the cross-cluster status update for all active clusters.

Frequently Asked Questions about monitor-cron-sweep

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

FAQPage Schema
How do I monitor job status across multiple Slurm clusters?▼

Cross-cluster job status monitoring consolidates squeue and sacct data from multiple clusters into a single unified report. It buckets jobs by type, such as RL, SFT, datagen, and eval, rendering a standardized table for tracking ongoing experiments.

How do I consolidate squeue and sacct data into a unified report?▼

Consolidating squeue and sacct data collects running and terminal state information across clusters, buckets jobs by type, and produces a deterministic job_monitor_table.md file. This standardized output supports tracking, auditing, and cross-cluster visibility.

Can I detect silent hangs and inode headroom issues with cluster job monitoring?▼

Cluster job monitoring identifies silent hangs, inode headroom issues, and per-type red flags by gathering queue and terminal state data. It flags completions or failures per job type and proposes appropriate cleanup or investigation steps.

How do I track RL, SFT, datagen, and eval jobs across different clusters?▼

Tracking RL, SFT, datagen, and eval jobs across clusters requires bucketing running and terminal states by type. The monitoring process renders a consolidated cross-cluster table, applying health flags and cleanup handoffs for each specific job category.

Does multi-cluster job monitoring require any specific dependencies or components?▼

Multi-cluster job monitoring requires no external dependencies or components to function. It directly gathers squeue and sacct data, verifies liveness, and renders cross-cluster tables to flag completions or failures per type.

What is the best way to flag completions and failures for multi-cluster experiments?▼

Flagging completions and failures for multi-cluster experiments involves collecting queue data and terminal states, then bucketing jobs by type. This approach identifies per-type red flags and generates cleanup handoffs within a standardized report.