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-latgit 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-lat)<a href="https://agentmods.dev/skills/nvidia/skills/doca-gpunetio-ib-write-lat"><img src="https://agentmods.dev/badge/skills/nvidia/skills/doca-gpunetio-ib-write-lat/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-lat"><img src="https://agentmods.dev/badge/skills/nvidia/skills/doca-gpunetio-ib-write-lat.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 | $0.00245 | $0.04126 |
| Opus 5 | $0.00122 | $0.02063 |
| Sonnet 5 | $0.00049 | $0.00825 |
| Haiku 4.5 | $0.00024 | $0.00413 |
Grade A, and why
doca-gpunetio-ib-write-lat 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 6d 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DOCA GPUNetIO ib_write_lat
Where to start: This is a tool skill for the GPUNetIO-
flavored ib_write_lat benchmark shipped under
doca/tools/gpunetio_ib_write_lat/ (a client + server pair,
built from source against the installed DOCA via meson).
It measures the latency of an RDMA WRITE work request when
the WR is posted from a CUDA kernel through the
doca-gpunetio device-side surface, in a ping-pong cadence.
Open TASKS.md and start at
## configure for the GPU-NIC pairing
precondition and the build pattern; jump to
## run for the single-iteration smoke
flow. Open CAPABILITIES.md when the
question is what this tool actually measures, how it
differs from the GPI sister tool on the same physical
operation, or how to interpret the half-iter / full-iter
/ CUDA-side usec output and the median / p99 / jitter
characterization. If DOCA is not installed yet, route to
doca-setup first; if the
user is still deciding between GPUNetIO and GPI as a
programming surface, the picture in
../../libs/doca-gpunetio/CAPABILITIES.md#capabilities-and-modes
and
../../libs/doca-gpi/CAPABILITIES.md#capabilities-and-modes
is the first stop.
Example questions this skill answers well
The CLASSES of doca-gpunetio-ib-write-lat 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 GPU-init RDMA-WRITE latency / jitter can the
GPUNetIO path deliver for a real-time / control-loop
workload?" — worked example: "measure per-iteration
WRITE latency between two hosts with an H100 +
ConnectX-7 on each side, target the median and the p99
separately". Answered by the GPU-NIC pairing
precondition in
CAPABILITIES.md ## Capabilities and modes- the bring-up flow in
TASKS.md ## configure+TASKS.md ## run.
- the bring-up flow in
- "This is the GPUNetIO tool — how does the latency
number differ from the GPI programming surface?" —
worked example: "the team is using GPI; should I expect
GPUNetIO to beat / tie / lose vs GPI?". Answered by the
"same physical operation, different runtime framework"
rule in
CAPABILITIES.md ## Capabilities and modes- the cross-link to the GPI library skill
../../libs/doca-gpi/CAPABILITIES.md(note:doca/tools/ships no GPIib_write_latbenchmark binary — GPI is a programming surface, not a shipped benchmark tool).
- the cross-link to the GPI library skill
- "Median vs p99 vs jitter — which one is the actual
answer for a real-time control loop?" — worked
example: "my control loop has a deadline; the median
is well under the budget but p99 spikes; do I quote
the median or the p99?". Answered by the
median-vs-p99-vs-jitter rule in
CAPABILITIES.md ## Observability- the eval-loop overlay in
TASKS.md ## test.
- the eval-loop overlay in
- "What is the latency-vs-batching trade-off specific
to GPU-init RDMA?" — worked example: "my CUDA kernel
could batch multiple WRs to amortize the GPU-side
overhead; what does that buy me on latency vs what does
it cost me?". Answered by the
latency-vs-batching trade-off in
CAPABILITIES.md ## Capabilities and modes. - "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_lateven link?". Answered by the version overlay inCAPABILITIES.md ## Version compatibility. - "How do I read the half-iter / full-iter / CUDA-side
usec columns?" — worked example: "the binary printed
half-iter, full-iter, and a CUDA-side number — what is
the right column to quote for one-way latency vs
round-trip vs cross-check?". Answered by the column-
semantics rule in
CAPABILITIES.md ## Observability.
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.
- 6d ago First seen · 325 lines · 245 tokens per session scan A 0ef5cce4622d
doca-gpunetio-ib-write-lat is a skill published in the GitHub repository NVIDIA/skills (3,244 stars, last pushed yesterday), licensed Apache-2.0. It adds 245 tokens to every session and 4,126 once invoked, about $0.0012 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 2026-09-03.
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