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-compress
description: >
Use this skill for hands-on DOCA Compress programming on a
BlueField DPU, ConnectX NIC, or host with DOCA — enabling
compress-deflate, decompress-deflate, decompress-lz4-stream,
or decompress-lz4-block tasks on a doca_compress context
(the hardware supports DEFLATE both directions plus LZ4
decompress; LZ4 encode is NOT supported), sizing source /
destination doca_buf against the per-task cap query, setting
mmap permissions, deciding offload vs CPU zlib / zstd,
validating with a round-trip smoke, or debugging
DOCA_ERROR_* from a Compress call. Trigger on phrasings
like "offload this gzip", "decompress incoming network
data", "compress task returns INVALID_VALUE on alloc_init",
"submitted a task but no completion arrives", or "decompress
LZ4 on the BlueField." Refuse and route elsewhere for
non-DEFLATE / non-LZ4 algorithms (zstd / Snappy / brotli),
LZ4 encode (route to a CPU LZ4 library), pure mmap-to-mmap
copies (doca-dma), or DOCA Core lifecycle internals.
metadata:
kind: library
compatibility: >
Requires DOCA SDK installed at /opt/mellanox/doca on Linux
(Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU or
ConnectX NIC attached. Reads the user's local install via
`pkg-config doca-compress` and inspects
/opt/mellanox/doca/{lib,include,samples,applications}.Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on Compress work (bulk DEFLATE
compression or decompression) on a BlueField / ConnectX / host
with DOCA. Open TASKS.md if the user wants to do
something (configure / build / modify / run / test / debug); open
CAPABILITIES.md when the question is what can
DOCA Compress express on this version. If the user has not
installed DOCA yet, route to
doca-setup first. If the user is
asking "should I even offload this compression to the
accelerator?", the size-threshold path-selection rule in
CAPABILITIES.md ## Capabilities and modes
is the first stop — bulk compress is the canonical fit, tiny
one-shot is not.
The CLASSES of DOCA Compress questions this skill is built to answer, each with one worked example. The agent should treat the class as the load-bearing piece — the worked example is a single instance.
CAPABILITIES.md ## Capabilities and modes
CAPABILITIES.md ## Safety policy.doca_compress_task_compress_deflate on
this BlueField, and what is the max source size per task?".
Answered by the per-task capability-query rule
(doca_compress_cap_task_compress_deflate_is_supported,
_decompress_deflate_is_supported, the matching
_get_max_buf_size queries) in
CAPABILITIES.md ## Capabilities and modes
TASKS.md ## configure.CAPABILITIES.md ## Capabilities and modes
task-type table + the per-task configuration matrix in
TASKS.md ## configure step 5.doca_compress_task_compress_deflate
returns DOCA_ERROR_NOT_PERMITTED". Answered by the permission
matrix in
CAPABILITIES.md ## Safety policy
TASKS.md ## test.doca_compress_task_decompress_deflate in the DOCA I have
installed?". Answered by the version-compatibility overlay in
CAPABILITIES.md ## Version compatibility,
which cross-links the canonical detection chain in
doca-version and adds the
Compress-specific "discover per-task support, do not assume"
bullets.DOCA_ERROR_* from a Compress call mean and
which layer caused it?" — worked example: "DOCA_ERROR_INVALID_VALUE
on doca_compress_task_compress_deflate_alloc_init". Answered
by the Compress overlay on the cross-library taxonomy in
CAPABILITIES.md ## Error taxonomy
TASKS.md ## debug that escalates to
doca-debug.This skill serves external developers building applications that
consume the DOCA Compress library — i.e., users whose code calls
doca_compress_* (directly in C/C++, or through FFI/bindings from
another language) to offload bulk DEFLATE compression or
decompression onto a BlueField DPU or ConnectX accelerator. It is
not for NVIDIA developers contributing to DOCA Compress itself.
Language scope. DOCA Compress ships as a C library with
pkg-config module name doca-compress. The shipped samples are
written in C. C and C++ consumers are the canonical case and the
worked examples in TASKS.md assume that path. Other-language
consumers (Rust, Go, Python, …) consume the same *.so through
FFI or language-specific bindings; the skill's contribution in
that case is to keep the lifecycle, capability-discovery,
permission, error-taxonomy, and compress-vs-decompress guidance
language-neutral, and to route the agent to the public C ABI as
the authoritative surface that any wrapper will eventually call.
Load this skill when the user is doing hands-on DOCA Compress work, in any language. Concretely:
doca_compress context on a doca_dev and
configuring at least one task type
(doca_compress_task_compress_deflate and/or
doca_compress_task_decompress_deflate) before
doca_ctx_start().doca_mmap correctly for the source
buffer (DOCA_ACCESS_FLAG_LOCAL_READ_ONLY at minimum) and the
destination buffer (DOCA_ACCESS_FLAG_LOCAL_READ_WRITE).doca_compress_cap_task_*_get_max_buf_size(devinfo) ceiling
and the destination buffer against the worst-case output size
the algorithm can produce on this input.doca_compress_cap_task_compress_deflate_is_supported and
doca_compress_cap_task_decompress_deflate_is_supported
against the active doca_devinfo.CAPABILITIES.md ## Capabilities and modes
says doca-compress is the right answer only for bulk inputs
(rule of thumb: ≥ a few KiB); below that, CPU compression beats
the DMA-to-accelerator round-trip.DOCA_ERROR_* returned from a Compress call
(lifecycle vs. buffer-sizing vs. permission vs.
unsupported-task) and the task-completion event on the progress
engine.Do not load this skill for general DOCA orientation, install
of DOCA itself, non-DEFLATE compression libraries on CPU (use
zlib / zstd / similar), or other DOCA libraries. For those, use
doca-public-knowledge-map.
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive Compress-specific material lives in two companion files:
CAPABILITIES.md — what DOCA Compress can express on this
version: the four task types (compress-deflate,
decompress-deflate, decompress-lz4-stream, and
decompress-lz4-block, each independently capability-gated), the
per-task capability-query surface (doca_compress_cap_* for
task support and per-submission max buffer size), the Compress
error taxonomy (mapped onto the cross-library DOCA_ERROR_*
set), the observability surface (per-task completion events on
the progress engine), the safety policy that gates source /
destination mmap permission decisions, and the size-threshold
path-selection rule (when to use doca-compress versus CPU
compression or doca-dma for the no-compression copy case).TASKS.md — step-by-step workflows for the six in-scope
Compress verbs: configure, build, modify, run, test,
debug. Plus a ## rollback overlay (Compress-specific
five-step teardown that drains in-flight tasks before
doca_ctx_stop, unregisters mmap regions in reverse-register
order, and re-verifies the device path with the round-trip
smoke) and the 5-phase universal debug-loop instantiation
appended to ## debug. Plus a Deferred task verbs block
that points out-of-scope questions at the right next skill.The skill assumes a host or BlueField where DOCA is already
installed at the standard location and the user has the
privileges their public install profile expects. It does not
cover installing DOCA — that path goes through
doca-setup.
This skill is agent guidance, not a samples or templates bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
/opt/mellanox/doca/samples/doca_compress/, plus
the File Compression reference application linked from the
public DOCA Compress guide. The agent's job is to route the
user to those files and prescribe a minimum-diff modification
on them via the universal modify-a-sample workflow in
doca-programming-guide,
layered with the Compress-specific overrides in
TASKS.md ## modify.zlib) as the known-good smoke; it does not ship
a fixture bank of its own.meson.build, CMakeLists.txt,
Cargo.toml, …) parked inside the skill. The agent constructs
the build manifest in the user's project directory against
the user's installed DOCA, where pkg-config --modversion doca-compress is the source of truth.samples/, bindings/, or reference/ subtree of any
kind. A mock or incomplete artifact in this skill's tree, even
one labeled "reference", is misleading: users will read it as
buildable.SKILL.md first to confirm the user's question is in
scope.Both companion files cross-link to each other,
doca-version for the canonical
version-handling rules, and
doca-public-knowledge-map
whenever the right answer is "look it up in the public docs or
the installed package layout" rather than "Compress-specific
guidance".
doca-public-knowledge-map —
the routing table for every public DOCA documentation source
and the on-disk layout of an installed DOCA package. The DOCA
Compress page lives at
docs.nvidia.com/doca/sdk/DOCA-Compress/; the File Compression
reference application is the canonical worked example.doca-setup — env preparation,
install verification, and the I have no install yet path
with the public NGC DOCA container. This skill assumes its
preconditions are satisfied.doca-version — canonical DOCA
version-handling rules. This skill's
## Version compatibility
cross-links the four-way match rule and adds only the
Compress-specific "discover per-task support + per-task max
buffer size via cap query" overlay.doca-structured-tools-contract —
the bundle's structured-tools precedence rule (detect / prefer
/ fall back / report). The Command appendix in
TASKS.md honors this contract.doca-programming-guide —
general DOCA programming patterns shared by every library: the
canonical pkg-config + meson build pattern, the universal
modify-a-shipped-sample first-app workflow, the universal
lifecycle, the cross-library DOCA_ERROR_* taxonomy, and the
program-side debug order. This skill layers Compress specifics
on top.doca-dma — the right library when
the data flow is a pure mmap-to-mmap copy with no compression
required. This skill's path-selection rule routes to DMA when
Compress is not the answer (e.g. the user only wants to move
bytes, not encode them).doca-debug — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). Compress-specific debug (task-not-supported,
source-buffer-too-large, destination-buffer-too-small) overlays
on top of that ladder.
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