复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Where to file an issue depends on what's broken:
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md — the skill instructions consumed by the agentskill-card.md — skill identity and governance cardskill.oms.sig — detached OMS signature (verifiable against nv-agent-root-cert.pem)evals/evals.json, evals/*.json, eval/*.json, or benchmark/evals.jsonBENCHMARK.md — generated benchmark report capturing verifiable uplift dataVerify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig — detached OMS-format signature (verifiable against nv-agent-root-cert.pem)skill-card.md — skill identity and governance cardevals/evals.json, evals/*.json, eval/*.json, or benchmark/evals.jsonWhen evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
This repository adheres to the Agent Skills specification:
SKILL.md file at their root.name and description fields.skills-ref reference library.Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
license: Apache-2.0
name: doca-dpa-hl-tracer
description: >
Use this skill when the user runs doca_dpa_hl_tracer to
capture/decode DPA-side traces at the programming-events
layer (kernel entry/exit, sync points, comm primitive
calls, RDMA WR submission, completion drain) — picking
TRACE vs CRIT, tuning the JSON config (file-size limits
+ file_size_limit_policy, thread priorities/cores),
decoding against the matching DPA-side ELF, or
diagnosing empty/noisy captures. Trigger even when the
user does not explicitly mention "DOCA DPA tracer" or
"high-level tracer" — typical implicit phrasings include
"DPA kernel returns wrong result but host completions
look clean", "kernel-entry to first-comm latency is
huge", "RDMA WR to drain gap on the DPA", "trace file
truncated mid-run", "TRACE doubled my DPA latency", or
"tracer wrote a file but parser shows zero events".
Refuse and route elsewhere for writing DPA kernels,
DPA-Comms/DPA-Verbs programming, raw per-cycle DPA
profiling, host-side doca-dpa debugging, or production
DPA telemetry — those belong to other skills.
metadata:
kind: tool
compatibility: >
Requires DOCA SDK installed at /opt/mellanox/doca on
Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField
device whose DPA processor is exposed to the host, plus
the DOCA DPA Tools optional component (binary at
/opt/mellanox/doca/tools/doca_dpa_hl_tracer). Requires a
DPACC-built DPA-side ELF and a live doca-dpa-launched
workload for events to fire.Where to start: This is a tool skill for invoking
doca_dpa_hl_tracer — the documented host-side CLI that
captures DPA-side execution traces in higher-level terms
(DPA programming events: kernel entry / exit, sync points,
comm primitive calls, RDMA WR submission, completions) rather
than raw cycle counts. Open TASKS.md and start at
## configure for the
mode-vs-overhead decision and the JSON config layout, then
## run for the
capture → decode → render pipeline. Open
CAPABILITIES.md when the question is
what does this tool actually trace, which DPA programming
events does it expose, what is the trace-overhead vs
fidelity tradeoff, or how does it slot into a DPA debug
loop alongside doca-dpa
and doca-debug. If DPA is not
the right surface for the user's question (e.g. the bug is
host-side, the bug is in the DPACC-produced image, the user
wants raw cycle counts), the path-selection rule in
CAPABILITIES.md ## Capabilities and modes
routes the agent before any capture is attempted.
The CLASSES of doca_dpa_hl_tracer 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.
doca_dpa_kernel_launch_update_* completes, but the
kernel's reported result is wrong; no host-side
DOCA_ERROR_*". Answered by the when DPA-side
high-level tracing is the right surface gate in
CAPABILITIES.md ## Capabilities and modes
TASKS.md ## run + the
which DPA programming events to focus on rule in
TASKS.md ## debug.CAPABILITIES.md ## Capabilities and modes
TASKS.md ## test which treats trace
overhead, mode (TRACE vs CRIT), and capture window
as axes to tune.TRACE mode
is producing too much data and my measured DPA latency
went up by 2x compared to without the tracer". Answered
by the mode-vs-overhead tradeoff in
CAPABILITIES.md ## Capabilities and modes
CRIT-first guidance in
TASKS.md ## configure (start
with critical-events-only; widen to TRACE only when the
bug demands per-event detail).log_file_max_size_in_bytes /
bin_file_max_size_in_bytes / file_size_limit_policy
triple in
CAPABILITIES.md ## Capabilities and modes
TASKS.md ## configure.doca-dpa library and DPACC compiler
version?" — worked example: "is the tracer ABI on my
install compatible with the DPA image my DPACC just
produced?". Answered by the version-overlay in
CAPABILITIES.md ## Version compatibility,
which redirects to the canonical
doca-version chain and
adds the tracer ↔ doca-dpa library ↔ DPACC compiler
match rule.doca_dpa_hl_tracer
ran, wrote a file, but the parser shows zero events".
Answered by the layered error taxonomy in
CAPABILITIES.md ## Error taxonomy
(install / device-binding / DPA-image-instrumented /
capture-window / decode-vs-elf / overhead-saturated /
version / cross-cutting) + the layered walk in
TASKS.md ## debug.This skill serves external developers, platform operators,
and AI agents who have already brought up a DPA-side workload
through doca-dpa and now
need higher-level visibility into what the DPA kernel is
actually doing on the wire — DPA programming events
ordering, sync gaps, comm-call latencies, RDMA-WR / completion
timing — without dropping all the way down to raw cycle
counters. Concretely:
doca-pcc) and needs to localize a regression to the
DPA side without instrumenting the application.doca-dpa TASKS.md ## debug
ladder when the host side reports clean completions but
the DPA-side behaviour is wrong.It is not for users debugging the tracer binary itself,
not a substitute for the live public DOCA DPA Tools
guide, not the right place for users learning how to
write a DPA kernel (that audience belongs in
doca-dpa plus the public
DOCA DPA / DPACC / DPA-Comms / DPA-Verbs guides), and not
the right place for raw per-instruction cycle profiling
(different surface, different tool — route via
doca-public-knowledge-map ## DOCA tools).
The tracer is shipped as a CLI binary under
/opt/mellanox/doca/tools/, not a library you link against.
The skill uses the same kind: tool three-file shape as
the rest of the bundle so the agent's task-verb contract is
uniform across libraries, services, and tools.
doca_dpa_hl_tracer is a C++ host-side CLI. Its inputs are
its JSON config file, the DPA-side ELF (the
doca_dpa_app-class image produced by DPACC), and a running
DPA-side workload that the host-side doca-dpa lifecycle
already started. Its outputs are a binary trace file
(bin_file) and a human-readable log file (log_file).
The skill keeps the workflow guidance language-neutral —
the DPA-side workload it traces can be C compiled by DPACC
or any other DPA translation unit DPACC accepts — and
routes per-language questions to the public DPA / DPACC
guides via
doca-public-knowledge-map.
Load this skill when the user is — or the agent needs to —
invoke doca_dpa_hl_tracer on a real host with DOCA
installed against a BlueField with a DPA processor visible to
the host, and the host-side
doca-dpa lifecycle has
already brought a DPA workload up at least once. Concretely:
doca-dpa lifecycle reports clean completions.TRACE and CRIT capture modes based
on the bug-vs-overhead tradeoff and the available
capture window.bin_file against the matching
DPA-side ELF to render the human-readable event stream.doca-dpa TASKS.md ## debug
ladder step.Do not load this skill for general DOCA orientation,
DPA-side programming model questions, raw cycle profiling,
or DOCA / DPACC install. For those, route to
doca-public-knowledge-map,
doca-dpa, or
doca-setup.
This is a thin loader. Substantive material lives in two companion files:
CAPABILITIES.md — what doca_dpa_hl_tracer captures: the
DPA programming event taxonomy (kernel entry / exit, sync
points, comm primitive calls, RDMA WR submission and
completion drain), the two documented capture modes
(TRACE for full per-event, CRIT for critical-events
only), the trace-overhead-vs-fidelity tradeoff, the
config-file shape (receiver / binary-writer / file-writer
/ printer threads with priority + core affinity, file
size limits, file_size_limit_policy), the
capture-window + workload-must-be-running invariant, the
ELF-must-match-image rule for decode, the
version-availability overlay (tracer ↔
doca-dpa library ↔
DPACC compiler), the layered error taxonomy
(install / device-binding / image-instrumented /
capture-window / decode / overhead-saturated / version /
cross-cutting), the observability surface (binary trace
file + log file + tool's own stderr), and the safety
policy (capture is bounded; tracing is not a production
observability surface).TASKS.md — step-by-step workflows for the in-scope
task verbs: install (route to host-side DOCA install +
DPA prerequisites), configure (mode + JSON config
layout + capture window), build (route to install —
the binary is shipped, the DPA-side application is
user-built by DPACC), modify (refuse — do not patch
the binary; modify the JSON config and the invocation
instead), run (the capture flow with --mode,
--config-file, --output-file), test (iterative
loop tuning mode, window, and overhead), debug (walk
the error taxonomy), use (consume the decoded trace
in a doca-dpa debug session), plus a Deferred task verbs block.The skill assumes a host where DOCA is already installed at
the standard location, a BlueField with a DPA processor is
present and visible to the host, the DPACC compiler is
installed at a version matched to the host-side DOCA, the
DPA-side application image (the ELF the tracer decodes
against) is on disk and matches what the
doca-dpa lifecycle loaded,
and the operator has the privileges the public DOCA DPA
Tools guide requires.
This skill is agent guidance, not a samples or scripts bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
--help
document. The DPA programming events surface evolves
release to release; --help on the installed binary is
the authoritative inventory.samples/ or reference/ subtree. This is a thin
loader for a shipped CLI; substantive material lives on
the public page, in --help, and in
doca-dpa.SKILL.md first to confirm the user's question
is in scope (DPA-side high-level tracing, not DPA-side
programming and not raw cycle profiling).install,
configure, build, modify, run, test, debug,
use — see TASKS.md.doca-dpa — the host-side
DPA control library whose loaded application image the
tracer captures. Pair them in every DPA debug session:
doca-dpa brings the workload up; the tracer captures
what the workload does at the DPA programming event
layer. Conflating the library with the tracer is the
most common DPA-debug first-touch error.doca-debug — the
cross-cutting debug ladder. The tracer slots in at the
runtime layer as the DPA-side ground truth before any
DPA-side perf or correctness conclusion is made.doca-public-knowledge-map
— routing to the public DOCA DPA Tools page on
docs.nvidia.com and the rest of the public DOCA
documentation set.doca-version — canonical
DOCA version-handling rules. The ## Version compatibility section in CAPABILITIES.md
is a concise overlay that redirects here for the body
and adds the tracer ↔ doca-dpa library ↔ DPACC
compiler matching rule.doca-setup — env
preparation, install verification, DPACC compiler
install / verification, BlueField mode (the DPA
processor must be exposed before any tracing is
meaningful), and the I have no install yet path with
the public NGC DOCA container.doca-structured-tools-contract
— the bundle's detect → prefer → fall back → report
contract for structured helper tools. The command
appendix in TASKS.md honors this contract.doca-programming-guide
— general DOCA programming patterns shared by every
library / tool surface, including the cross-library
DOCA_ERROR_* taxonomy this tool's host-side error
layer overlays on top of when host-side doca-dpa
calls fail in tandem.The DPA-side companion libraries doca-dpa-comms (comm
primitives the DPA kernel itself calls) and
doca-dpa-verbs (RDMA verbs the DPA kernel itself calls)
are different artifacts that the tracer's DPA
programming events surface visibly names; for the
DPA-side programming model itself, route through
doca-public-knowledge-map
to the public DOCA DPA-Comms and DPA-Verbs guides and to
the shipped /opt/mellanox/doca/samples/doca_dpa/ samples.
This tool traces their use; it does not redefine them.
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