NVIDIA/skills is a catalogue of portable instruction sets that teach coding agents how to use NVIDIA software for robotics, simulation, CUDA, retrieval-augmented generation, and related workflows. Developers install these skills in agents such as Claude Code or Codex, while the catalogue mirrors skills maintained in separate NVIDIA product repositories.
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.
npx skills add NVIDIA/skills --skill doca-gpunetio-ib-write-bwgit clone --depth 1 https://github.com/NVIDIA/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/skills/doca-gpunetio-ib-write-bw)<a href="https://agentmods.dev/skills/nvidia/skills/doca-gpunetio-ib-write-bw"><img src="https://agentmods.dev/badge/skills/nvidia/skills/doca-gpunetio-ib-write-bw/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/skills/doca-gpunetio-ib-write-bw"><img src="https://agentmods.dev/badge/skills/nvidia/skills/doca-gpunetio-ib-write-bw.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00208 | $0.04368 |
| Opus 5.5 | $0.00083 | $0.01747 |
| Sonnet 5.5 | $0.00042 | $0.00874 |
| Haiku 4.5 | $0.00021 | $0.00437 |
Grade A, and why
doca-gpunetio-ib-write-bw 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 15d 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.
How it starts
The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DOCA GPUNetIO ib_write_bw
Examples
- "What sustained RDMA-WRITE bandwidth can GPUNetIO deliver on
this GPU-NIC pair?" — this is in scope. Stay on
gpunetio_ib_write_bw; do not retune a live GPI app here. - "Where is the bottleneck — GPU occupancy, NIC issue rate, or link saturation?" — decompose throughput before changing the CUDA kernel.
Instructions
Follow Loading order below. Open the tasks companion for configure / run / debug. Open the capabilities companion for GPU-NIC pairing and observability. Do not open every companion file.
Where to start: This is a tool skill for the GPUNetIO-
flavored ib_write_bw benchmark shipped under
doca/tools/gpunetio_ib_write_bw/ (a client + server pair,
built from source against the installed DOCA via meson).
It measures sustained RDMA WRITE bandwidth when the WRs are
posted from a CUDA kernel through the doca-gpunetio
device-side surface, with the GPU on the data path. Open
the tasks companion and start at
## configure for the GPU-NIC
pairing precondition and the build pattern; jump to
## run for the smoke-before-bulk flow.
Open the capabilities companion when the question
is what this tool actually measures, how the result
decomposes (GPU occupancy vs NIC issue rate vs link
saturation), or how the result reads against the GPI
sister tool and the upstream CPU-initiated perftest
ib_write_bw. If DOCA is not installed yet, route to
doca-setup/SKILL.md first; if the
user is still deciding between the GPI and GPUNetIO
programming surfaces, the picture in
doca-gpunetio/SKILL.md
and
doca-gpi/SKILL.md
is the first stop.
Example questions this skill answers well
The CLASSES of doca-gpunetio-ib-write-bw questions this
skill is built to answer, each with one worked example. The
class is the load-bearing piece; the worked example is one
instance.
- "What sustained RDMA-WRITE bandwidth can the GPUNetIO
path deliver on this GPU-NIC pair?" — worked example:
"measure sustained WRITE BW between two hosts with an
H100 + ConnectX-7 on each side". Answered by the
GPU-NIC pairing precondition in
the capabilities companion
- the bring-up flow in the tasks companion + the tasks companion. The same shape answers "measure GPUNetIO-driven WRITE BW between a host GPU and a BlueField DPU".
- "Where is the bottleneck — GPU compute occupancy, NIC
issue rate, or link saturation?" — worked example:
"I see 120 Gbit/s on a 200 Gbit/s link; is the NIC
saturated, am I CPU-bound on the client, or is the CUDA
kernel not driving enough WRs in flight?". Answered by
the throughput-decomposition rules in
the capabilities companion
- the eval-loop overlay in the tasks companion.
- "How does the result differ from the classic CPU-
initiated
perftestib_write_bw?" — worked example: "my team has a CPU-initiated WRITE BW number on this same NIC; should I expect the GPUNetIO number to match or be different?". Answered by the "GPU-initiated path adds (or removes) overhead vs the CPU-initiated path" rule in the capabilities companion. - "Is the doca-gpunetio path the right surface for my
sustained-throughput workload class?" — worked example:
"my application streams sensor data from GPU memory at
line rate to a remote consumer". Answered by the
"when GPUNetIO is the right surface vs GPI vs CPU-
initiated" rule in
the capabilities companion
- the use-side decision in the tasks companion.
- "My BW number swings between runs. What do I check before quoting it?" — worked example: "three runs at the same flags gave 145, 187, and 160 Gbit/s; is the benchmark noisy or is my platform inconsistent?". Answered by the measurement-soundness rules in the capabilities companion layer 5 + the steady-state guidance in the tasks companion.
- "What version of DOCA + CUDA Toolkit do I need for this
binary to build and run?" — worked example: "my
install has DOCA at one semver and CUDA at another; will
the ToT-shipped
gpunetio_ib_write_bweven link?". Answered by the version overlay in the capabilities companion which cross-links the canonical detection chain indoca-version/SKILL.md.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 15d ago Changed · -1 lines · -41 tokens per session 059c538412fb
- 23d ago Changed · +20 lines 2b0a82fe7647
- 2mo ago First seen · 354 lines · 249 tokens per session scan A d8c93fa1d19b
doca-gpunetio-ib-write-bw is a skill published in the GitHub repository NVIDIA/skills (3,873 stars, last pushed yesterday), licensed Apache-2.0. It adds 208 tokens to every session and 4,368 once invoked, about $0.0008 per session on Opus 5.5. 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 2026-09-03.
doca-gpunetio-ib-write-bw compared with skills from other repositories
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|---|---|---|---|---|---|---|---|
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