How do I install the Nvidia AI Networking Fabric Review skill?

Run npx skillmds@latest add vincentchuwaichow/nvidia-ai-networking-fabric-review 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 Networking Fabric Review safe to use?

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Which AI agents work with Nvidia AI Networking Fabric Review?

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 Networking Fabric Review free to use?

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

Who published Nvidia AI Networking Fabric Review?

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

Nvidia AI Networking Fabric Review by vincentchuwaichow

Name: Nvidia AI Networking Fabric Review
Author: VincentChuWaiChow

Nvidia AI Networking Fabric Review

Use this skill when reviewing NVIDIA AI networking fabrics — Spectrum-X Ethernet or InfiniBand topology, NCCL collective tuning, RoCEv2 lossless DCQCN/PFC config, adaptive routing, congestion control, and east-west isolation between training jobs. Trigger when the user asks whether the fabric is non-blocking, whether NCCL chooses the right transport, whether PFC/ECN is configured correctly, or whether the deployment meets NCP-AIN expectations.

File contents

NVIDIA AI Networking Fabric Review

Purpose

Review NVIDIA AI fabric configuration against the NCP-AIN body of knowledge: rail-optimized topology, NCCL collective communication tuning (NCCL_TOPO, NCCL_IB_HCA, NCCL_NET_GDR_LEVEL), RoCEv2 lossless DCQCN/PFC, InfiniBand subnet manager and partitioning, adaptive routing, and tenant/job east-west isolation.

Lean operating rules

Prefer live evidence (ibstat, ibdiagnet, nccl-tests all_reduce_perf baselines, ethtool -S, switch QoS counters) when the active client exposes it; otherwise fall back to NVIDIA Spectrum-X / Quantum InfiniBand documentation and sanitized topology diagrams.
Separate confirmed facts from inference. If NCCL transport selection, PFC posture, or partition keys were not directly queried, say so.
Treat a non-rail-optimized topology presented as rail-optimized as a critical finding — collective performance claims are wrong.
Treat RoCEv2 deployed without PFC + ECN (DCQCN) as a high finding — lossless behavior is not provided; goodput collapses under congestion.
Treat default NCCL settings on multi-rail systems as a medium finding when published baselines are missing — transport selection is unverified.
Treat shared partition keys (PKey 0x7FFF / default) across tenants on a multi-tenant InfiniBand fabric as a high finding — east-west isolation is absent.
Treat absence of nccl-tests baselines stored alongside the cluster spec as a medium finding — regressions cannot be detected.
Treat subnet manager running on a single switch with no failover as a high finding for production fabrics.

Response minimum

Return, at minimum:

the scoped target (fabric type, generation, scale) and evidence level,
topology and rail posture,
NCCL transport and tuning posture,
lossless/PFC/ECN posture (RoCEv2) or PKey/partition posture (InfiniBand),
tenant isolation posture,
safe next actions and assumptions or blockers.

---name: nvidia-ai-networking-fabric-reviewdescription: Use this skill when reviewing NVIDIA AI networking fabrics — Spectrum-X Ethernet or InfiniBand topology, NCCL collective tuning, RoCEv2 lossless DCQCN/PFC config, adaptive routing, congestion control, and east-west isolation between training jobs. Trigger when the user asks whether the fabric is non-blocking, whether NCCL chooses the right transport, whether PFC/ECN is configured correctly, or whether the deployment meets NCP-AIN expectations.---# NVIDIA AI Networking Fabric Review## PurposeReview NVIDIA AI fabric configuration against the NCP-AIN body of knowledge: rail-optimized topology, NCCL collective communication tuning (NCCL_TOPO, NCCL_IB_HCA, NCCL_NET_GDR_LEVEL), RoCEv2 lossless DCQCN/PFC, InfiniBand subnet manager and partitioning, adaptive routing, and tenant/job east-west isolation.## Lean operating rules- Prefer live evidence (`ibstat`, `ibdiagnet`, `nccl-tests` `all_reduce_perf` baselines, `ethtool -S`, switch QoS counters) when the active client exposes it; otherwise fall back to NVIDIA Spectrum-X / Quantum InfiniBand documentation and sanitized topology diagrams.- Separate confirmed facts from inference. If NCCL transport selection, PFC posture, or partition keys were not directly queried, say so.- Treat a non-rail-optimized topology presented as rail-optimized as a critical finding — collective performance claims are wrong.- Treat RoCEv2 deployed without PFC + ECN (DCQCN) as a high finding — lossless behavior is not provided; goodput collapses under congestion.- Treat default NCCL settings on multi-rail systems as a medium finding when published baselines are missing — transport selection is unverified.- Treat shared partition keys (PKey 0x7FFF / default) across tenants on a multi-tenant InfiniBand fabric as a high finding — east-west isolation is absent.- Treat absence of `nccl-tests` baselines stored alongside the cluster spec as a medium finding — regressions cannot be detected.- Treat subnet manager running on a single switch with no failover as a high finding for production fabrics.## Response minimumReturn, at minimum:- the scoped target (fabric type, generation, scale) and evidence level,- topology and rail posture,- NCCL transport and tuning posture,- lossless/PFC/ECN posture (RoCEv2) or PKey/partition posture (InfiniBand),- tenant isolation posture,- safe next actions and assumptions or blockers.

Frequently asked questions