How do I install the Nvidia AI Operations Day2 skill?

Run npx skillmds@latest add vincentchuwaichow/nvidia-ai-operations-day2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Nvidia AI Operations Day2 safe to use?

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Which AI agents work with Nvidia AI Operations Day2?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Nvidia AI Operations Day2 free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Nvidia AI Operations Day2?

VincentChuWaiChow (@vincentchuwaichow) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Nvidia AI Operations Day2 by vincentchuwaichow

Name: Nvidia AI Operations Day2
Author: VincentChuWaiChow

Nvidia AI Operations Day2

Use this skill when reviewing day-2 operations of NVIDIA GPU fleets — DCGM exporter and DCGM-Diag posture, GPU telemetry into Prometheus/Grafana, MIG partitioning lifecycle, GPU health and Xid error response, fleet upgrade paths, and incident response for GPU-failure modes. Trigger when the user asks how a GPU fleet is monitored, whether DCGM is wired, whether Xid signatures map to runbooks, or whether the deployment meets NCP-AIO certification expectations.

File contents

NVIDIA AI Operations (Day-2) Review

Purpose

Review operational posture of NVIDIA GPU fleets against the NCP-AIO body of knowledge: DCGM and DCGM-Exporter coverage, MIG partitioning lifecycle, Xid error classification and runbooks, fleet driver/firmware upgrade orchestration, and GPU-aware incident response.

Lean operating rules

Prefer live evidence (dcgmi diag, dcgmi health, nvidia-smi --query-gpu=..., Prometheus DCGM metrics) when the active client exposes it; otherwise fall back to NVIDIA DCGM documentation, sanitized dashboards, and Xid reference tables.
Separate confirmed facts from inference. If DCGM coverage, alerting rules, or Xid classification was not directly queried, say so.
Treat absence of DCGM exporter on production GPU nodes as a high finding — fleet is operationally blind.
Treat ungated driver/firmware rolling upgrade (no pre-drain, no canary, no rollback path) as a high finding.
Treat undocumented Xid signature → runbook mapping as a medium finding — incidents will be triaged from scratch.
Treat GPU-Operator / device-plugin running with default permissive securityContext on multi-tenant clusters as a high finding.
Treat MIG re-partitioning performed live without workload drain as a medium finding (running contexts are killed).
Treat alerting that does not cover ECC double-bit errors, NVLink errors, or row-remapping pending as a medium finding.

Response minimum

Return, at minimum:

the scoped target (cluster/fleet, exporter version) and evidence level,
DCGM exporter coverage and alert posture,
MIG partitioning lifecycle posture,
Xid → runbook coverage,
driver/firmware upgrade discipline,
safe next actions and assumptions or blockers.

---name: nvidia-ai-operations-day2description: Use this skill when reviewing day-2 operations of NVIDIA GPU fleets — DCGM exporter and DCGM-Diag posture, GPU telemetry into Prometheus/Grafana, MIG partitioning lifecycle, GPU health and Xid error response, fleet upgrade paths, and incident response for GPU-failure modes. Trigger when the user asks how a GPU fleet is monitored, whether DCGM is wired, whether Xid signatures map to runbooks, or whether the deployment meets NCP-AIO certification expectations.---# NVIDIA AI Operations (Day-2) Review## PurposeReview operational posture of NVIDIA GPU fleets against the NCP-AIO body of knowledge: DCGM and DCGM-Exporter coverage, MIG partitioning lifecycle, Xid error classification and runbooks, fleet driver/firmware upgrade orchestration, and GPU-aware incident response.## Lean operating rules- Prefer live evidence (`dcgmi diag`, `dcgmi health`, `nvidia-smi --query-gpu=...`, Prometheus DCGM metrics) when the active client exposes it; otherwise fall back to NVIDIA DCGM documentation, sanitized dashboards, and Xid reference tables.- Separate confirmed facts from inference. If DCGM coverage, alerting rules, or Xid classification was not directly queried, say so.- Treat absence of DCGM exporter on production GPU nodes as a high finding — fleet is operationally blind.- Treat ungated driver/firmware rolling upgrade (no pre-drain, no canary, no rollback path) as a high finding.- Treat undocumented Xid signature → runbook mapping as a medium finding — incidents will be triaged from scratch.- Treat GPU-Operator / device-plugin running with default permissive securityContext on multi-tenant clusters as a high finding.- Treat MIG re-partitioning performed live without workload drain as a medium finding (running contexts are killed).- Treat alerting that does not cover ECC double-bit errors, NVLink errors, or row-remapping pending as a medium finding.## Response minimumReturn, at minimum:- the scoped target (cluster/fleet, exporter version) and evidence level,- DCGM exporter coverage and alert posture,- MIG partitioning lifecycle posture,- Xid → runbook coverage,- driver/firmware upgrade discipline,- safe next actions and assumptions or blockers.

Frequently asked questions