monitor

A job-status checker for SLURM, a system that runs computing jobs on shared clusters. It covers jobs such as model evaluation, quantization, deployment, and other submitted tasks.

In plain words
What is it for?
Use it to check a job’s status, monitor an evaluation or deployment, or inspect SLURM commands such as squeue.
Why use it?
It helps you find out whether a long-running cluster job is queued, running, finished, or needs attention.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/nvidia/model-optimizer/monitor
Any agent
npx skills add NVIDIA/Model-Optimizer --skill monitor
Clone the repo
git clone --depth 1 https://github.com/NVIDIA/Model-Optimizer

Made for: Claude Code, Codex.

Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,809 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00093 $0.01809
Opus 5 $0.00046 $0.00905
Sonnet 5 $0.00019 $0.00362
Haiku 4.5 $0.00009 $0.00181

Measured 2d ago against content hash d3f15c54d135, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

monitor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/modelopt/skills/monitor/SKILL.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Job Monitor

Monitor jobs submitted to SLURM clusters — PTQ quantization, NEL evaluation, model deployment, or raw SLURM jobs.

When to use

  1. Auto-monitor — another skill (PTQ, evaluation, deployment) just submitted a job. Register the job and set up monitoring immediately.
  2. User-initiated — user asks about a job status. Check the current session registry first; if the job is not registered there, use the discovery steps below.

Job Registry

Active jobs are tracked in per-session registries under .claude/agents/. This avoids multiple agents clobbering one shared registry when they run at the same time.

Use the current agent session id as <session_id>:

  • Claude Code: $CLAUDE_CODE_SESSION_ID, or the session_id field from hook input
  • Codex: $CODEX_THREAD_ID
  • If no session id is available, create a stable id for the current terminal session and reuse it for every job registered by that agent

Registry layout:

.claude/agents/
  <session_id>/
    active_jobs.json

Each session's active_jobs.json is a JSON array:

[
  {
    "type": "nel",
    "id": "<invocation_id or slurm_job_id>",
    "host": "<cluster_hostname>",
    "user": "<ssh_user>",
    "submitted": "YYYY-MM-DD HH:MM",
    "description": "<what this job does>",
    "last_status": "<last known status>",
    "owner": {
      "agent": "claude-code|codex|manual",
      "session_id": "<session_id>"
    }
  }
]

type is one of: nel, slurm, launcher.


On Job Submission

Every time a job is submitted (by any skill or manually):

  1. Add an entry to .claude/agents/<session_id>/active_jobs.json. Create the session directory and file if they don't exist.
  2. Start a durable monitor (if one isn't already watching the registry) that polls this session's registered jobs until they reach terminal status. Prefer the Claude Code Monitor tool when it is available: write a small watcher that reads .claude/agents/<session_id>/active_jobs.json, checks every job with the appropriate method below, prints state-change events, updates last_status, removes terminal jobs from the session registry, and exits when no active jobs remain for this session.

Read the full file on GitHub · 172 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 172 lines · 93 tokens per session scan A d3f15c54d135

Subscribe to this mod's changes

monitor is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,612 stars, last pushed 2d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,809 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens