Monitor submitted jobs (PTQ, evaluation, deployment) on SLURM clusters. Use when the user asks "check job status", "is my job done", "monitor my evaluation", "what's the status of the PTQ", "check on job <slurm_job_id>", or after any skill submits a long-running job. Also triggers on "nel status", "squeue", or any request to check progress of a previously submitted job.
npx skills add https://github.com/NVIDIA/Model-Optimizer --skill monitor
Monitor jobs submitted to SLURM clusters — PTQ quantization, NEL evaluation, model deployment, or raw SLURM jobs.
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>:
$CLAUDE_CODE_SESSION_ID, or the session_id field from hook input$CODEX_THREAD_IDRegistry 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.
Every time a job is submitted (by any skill or manually):
.claude/agents/<session_id>/active_jobs.json. Create the session directory and file if they don't exist.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.The monitor should terminate naturally when every registered job has reached a terminal state. If the Monitor tool is not available in the current harness, run an equivalent background process that implements the same loop and lets the agent resume/restart when the process exits.
Always do both steps. Don't try to predict job duration.
Whether triggered by monitor output or by the user asking "check status":
.claude/agents/<session_id>/active_jobs.jsonlast_status in registrylast_status in the session registryEach check method has its own status vocabulary. A watcher that mixes them
(e.g. uses SLURM's COMPLETED terminal-state regex against nel status output)
will silently never fire terminal transitions. Always match against the
vocabulary of the source you're polling.
type: nel)nel status <id>.extract_nel_state() {
local jid="$1" nel_bin="${NEL:-nel}" output state_col
output=$("$nel_bin" status "$jid" 2>&1)
state_col=$(echo "$output" \
| awk -F'|' -v prefix="$jid." 'index($1, prefix) == 1 { print $2; exit }')
[ -z "$state_col" ] && state_col="$output"
echo "$state_col" \
| LC_ALL=C tr '[:lower:]' '[:upper:]' \
| awk 'match($0, /(PENDING|RUNNING|SUCCESS|FAILED|KILLED|ERROR|NOT[[:space:]]+FOUND)/) { print substr($0, RSTART, RLENGTH); exit }' \
| sed 's/[[:space:]][[:space:]]*/ /g'
}
is_nel_terminal() {
case "$(extract_nel_state "$1")" in
SUCCESS|FAILED|KILLED|ERROR|"NOT FOUND") return 0 ;;
*) return 1 ;;
esac
}
nel info <id> to fetch results.nel info <id> --logs then inspect server/client/SLURM logs via SSH.type: launcher)Traceback, Error, or FAILED in the output.type: slurm)sacct; use sacct for the termination check because squeuecan lag in COMPLETING after sacct reports a terminal state.
extract_slurm_state() {
local jid="$1" host="$2"
ssh "$host" "sacct -j $jid -X --format=State --noheader -P 2>/dev/null | head -1" \
| sed 's/^[[:space:]]*//;s/[[:space:]]*$//' \
| sed 's/^CANCELLED by .*/CANCELLED/'
}
is_slurm_terminal() {
case "$(extract_slurm_state "$1" "$2")" in
COMPLETED|FAILED|CANCELLED|TIMEOUT|NODE_FAIL|OUT_OF_MEMORY|PREEMPTED|BOOT_FAIL|DEADLINE) return 0 ;;
*) return 1 ;;
esac
}
ssh <host> "sacct -j <id> --format=State,ExitCode,Elapsed -n".When the user asks about a job without specifying an ID, check in order:
.claude/agents/<current_session_id>/active_jobs.json — current agent's jobsnel ls runs --since 1d — recent NEL runsssh <host> "squeue -u <user>" — active SLURM jobsls -lt tools/launcher/experiments/cicd/ | head -10 — recent launcher experimentsAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take nvidia/monitor from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.