LLM Torch Profiler Analysis
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
Build and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware…
$ npx skills add NVIDIA/skills --skill doca-flow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills doca-flow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/doca-flow .claude/skills/doca-flow && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "doca-flow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-flow into .claude/skills/doca-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-flow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/doca-flowType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill doca-flow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills doca-flow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/doca-flow .agents/skills/doca-flow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "doca-flow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-flow into .agents/skills/doca-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-flow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill doca-flow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills doca-flow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/doca-flow .cursor/skills/doca-flow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "doca-flow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-flow into .cursor/skills/doca-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-flow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/doca-flow--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill doca-flow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills doca-flow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/doca-flow .gemini/skills/doca-flow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "doca-flow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-flow into .gemini/skills/doca-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-flow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills doca-flowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill doca-flow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/doca-flow .github/skills/doca-flow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "doca-flow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-flow into .github/skills/doca-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-flow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill doca-flow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills doca-flow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/doca-flow .opencode/skills/doca-flow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "doca-flow" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-flow into .opencode/skills/doca-flow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-flow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
doca-flowBuild and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware…
Doca Flow is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Build and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware programming, read counters, match the Flow version to the installed DOCA release, and diagnose Flow API errors. Trigger on DOCA packet steering, classifier, representor, rule-matching, hairpin, or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Route plain DPDK rteflow, kernel TC, OVS, BFB bring-up, and DPU OS…
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files (for example `BENCHMARK.md`, `CAPABILITIES.md` and `SKILLCARD.yaml`). Compatibility notes: Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a supported NVIDIA NIC/DPU attached. Reads the user's local…
It works with NVIDIA AI Platform. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a supported NVIDIA NIC/DPU attached. Reads the user's local install via `pkg-config doca-flow` and inspects /opt/mellanox/doca/{lib,include,samples,applications}.
From compatibility in the SKILL.md frontmatter.
Doca Flow loads about 3.4k tokens when it runs. Until then it costs about 158 tokens; SKILL.md has 1,478 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 1,478 words, ~3,423 tokens.
.claude/skills/doca-flow/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.When this skill is in scope, the user is asking for DOCA Flow. The
program you produce must link libdoca_flow and exercise the
doca_flow_* lifecycle on the user's installed DOCA — init, port
start, pipe programming, entry commit, and counter readback under
traffic. Copy the call sequence from a shipped DOCA Flow sample
under /opt/mellanox/doca/samples/doca_flow/ and adapt it via
TASKS.md ## configure /
TASKS.md ## modify. Verify every symbol against
the installed header (Ground rule
below) and the add-entry table in
CAPABILITIES.md ## API surface and name guards.
Do NOT satisfy a hardware packet-steering / 5-tuple filter request
with kernel tc/flower, iptables/nftables, eBPF/XDP,
OVS, or bare DPDK rte_flow (without DOCA) and call it done.
Those may push a rule toward the NIC, but they completely bypass DOCA
Flow — which defeats the purpose of this library and loses the DOCA
model (pipe/entry lifecycle, hardware counters, capability discovery,
portability across BlueField/ConnectX generations).
"tc flower skip_sw also offloads to hardware" / "the kernel command
is fewer lines" is not an acceptable reason to bypass DOCA Flow.
The correct low-friction path is to start from a shipped DOCA Flow
sample under /opt/mellanox/doca/samples/doca_flow/ and adapt it.
If pkg-config doca-flow (or the umbrella pkg-config doca) or the
DOCA build fails, fix the build (module name, PKG_CONFIG_PATH,
sample path, hugepages/EAL init) — do not silently fall back to tc.
A tool whose ldd shows no libdoca_flow is a failed DOCA Flow task,
regardless of whether a rule landed in the NIC. Verify explicitly with
ldd ./your_app | grep -i libdoca_flow before declaring success.
Where to start: Open TASKS.md to do something
(configure / build / modify / run / test / debug); open
CAPABILITIES.md when the question is what can
Flow express on this version. You MUST open
TASKS.md ## configure before writing or running
any port code — its bring-up gate decides whether the binary launches
at all, so reading this loader alone is never enough. If DOCA is not
installed yet, route to doca-setup first.
Before quoting any doca_* / DOCA_* identifier, confirm it exists in
the user's installed headers — the header on the machine is ground
truth above prose, the API reference, blog posts, or memory:
for header in "$(pkg-config --variable=includedir doca-common)"/doca_flow*.h; do
grep -n '<candidate_name>' "$header"
# For a multi-line function declaration, print through its closing `);`.
awk '/<candidate_name>[[:space:]]*\(/,/[)][[:space:]]*;/' "$header"
doneDOCA Flow ships no backward-compat alias header, so a
"reasonable-looking" name that is not in the header simply does not
link. Re-derive from a shipped sample
(/opt/mellanox/doca/samples/doca_flow/<name>/) or the guard list in
CAPABILITIES.md ## API surface and name guards,
never from prose.
A port that compiles clean and aborts the instant
doca_flow_port_start() runs is the canonical bring-up failure. The
bring-up gate (probe-before-count, doca_flow_port_cfg_set_port_id() plus
the mode-appropriate device source — doca_flow_port_cfg_set_dev() in
VNF mode or the installed switch sample's doca_dev_rep path — device
taken from launch args not hard-coded, and the binary returning a
non-zero exit from main() if the bridge cannot arm and forward) is
enforced step-by-step in
TASKS.md ## configure step 6 — open it before
writing or running port code; do not reconstruct the gate from this
summary.
Some requests cannot be satisfied as asked. Refuse and explain — do not silently emit half-correct code — when:
CAPABILITIES.md ## Pipe decomposition).tc, iptables/nftables, eBPF/XDP, OVS, or bare
rte_flow without DOCA). Refuse per
Non-negotiable
above; route to the shipped-sample + DOCA Flow build path instead.Output shape when pushing back:
REFUSED: <one-sentence summary>
Reason: <2-4 bullets, each tied to a hardware or API constraint>
Suggested alternative: <pipe-graph sketch, or "this is not expressible in DOCA Flow">This gate fires before any code is written: a confidently-wrong pipe costs the user more than an honest refusal plus the legal alternative.
The CLASSES of Flow questions this skill answers (the class is the load-bearing piece; the example is one instance):
TASKS.md ## configure +
CAPABILITIES.md ## Capabilities and modes.CAPABILITIES.md ## Capabilities and modes
CAPABILITIES.md ## Safety policy
CAPABILITIES.md ## Observability
Failed to get hws cap / dest action ROOT … err -121." → device-placement signature (not a pipe bug);
the steering plane belongs to the DPU Arm on a SEPARATED_HOST /
NIC-mode BlueField. See the device-placement bullet in
CAPABILITIES.md ## Capabilities and modesTASKS.md ## configure step 2 + Step 0 of
TASKS.md ## debug.CAPABILITIES.md ## flow-ct +
TASKS.md ## flow-ct.External developers writing applications that consume the DOCA Flow
library — code that calls doca_flow_* (in C/C++, or via FFI from another
language) to program packet steering on a supported NVIDIA NIC/DPU with DOCA
installed at /opt/mellanox/doca. Flow ships as a C library (pkg-config
module doca-flow, package doca-sdk-flow on Ubuntu / RHEL / SLES) and the
samples are C, so C/C++ is the canonical path the TASKS.md examples assume;
other-language consumers reach the same *.so through FFI, and the skill
keeps its API-surface, lifecycle, capability-discovery, error-taxonomy, and
safety guidance language-neutral.
Load when the user is doing hands-on DOCA Flow work on a supported
NVIDIA NIC/DPU with DOCA already installed at /opt/mellanox/doca, in
any language:
pkg-config --modversion doca-flow is the build-time anchor).DOCA_ERROR_* from a Flow call (config mistake vs
missing prerequisite vs unsupported on this hardware / install).doca_flow_ct.h) on top of an existing port
(see TASKS.md ## flow-ct).Do not load for general DOCA orientation, "where do I find docs",
install-layout, or non-Flow library questions — use
doca-public-knowledge-map.
This is a thin loader; substantive material lives in two companion files:
CAPABILITIES.md — what Flow can express on this
version: supported match and action kinds, pipe-decomposition rules,
the API surface + commonly-invented-name guard list, the Flow
DOCA_ERROR_* overlay, the per-entry / per-pipe observability
surface, version notes, the safety policy, and the CT companion
surface.TASKS.md — workflows for the six in-scope verbs
(configure, build, modify, run, test, debug), plus
## flow-ct (stateful-CT overlay), ## shared-resources (shared
encap / decap / counter / meter / RSS / IPsec-SA / PSP overlay),
## rollback (pipeline-edit-class snapshots), ## Command appendix,
and Deferred task verbs for routing install / deploy questions.The skill assumes DOCA is installed at /opt/mellanox/doca and the user can
open a doca_dev. Installing DOCA, hugepages setup, and the EAL dv_flow_en
devargs prep go through doca-setup; the
hugepages / devargs runtime prerequisites a binary needs before a Flow port
starts are pinned in TASKS.md ## configure step 5.
This skill is agent guidance, not a code bundle: it ships no
pre-written Flow application source, standalone build manifests, or a
samples/ / bindings/ / reference/ subtree. The verified Flow
source is the shipped C sample at
/opt/mellanox/doca/samples/doca_flow/<name>/ — the agent routes the
user there and prescribes a minimum-diff edit via the modify-a-sample
workflow in
doca-programming-guide plus
the Flow overrides in TASKS.md ## build, and builds
any manifest in the user's project against the user's install, where
pkg-config --modversion doca-flow is the source of truth.
SKILL.md first to confirm the question is in scope.CAPABILITIES.md.TASKS.md.doca-public-knowledge-map —
routing table for public DOCA docs and the on-disk layout of an
installed package.doca-setup — env prep, install
verification, and the no install yet path via the NGC DOCA
container.doca-programming-guide —
general DOCA patterns shared by every library: the pkg-config +
meson build pattern, the 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 Flow specifics
on top.doca-flow-tune —
programmed-state inspection (read-only).doca-hardware-safety —
required overlay for card-mode flips (e.g. mlxconfig change from
SEPARATED_HOST to EMBEDDED_CPU).doca-debug — the cross-cutting debug
ladder (install / version / build / link / runtime / program /
driver).© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 7 other files in skills/doca-flow of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
Doca Flow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Doca Flow this skillNVIDIA/skills | 3.5k | — | ~3.4k | Automated safety check: Pass | Apache-2.0 | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Skill InspectorNVIDIA/SkillSpector | 20k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Embeddings via 9Routerdecolua/9router | 30k | — | ~604 | Automated safety check: Pass | MIT | |
| NEAR AI Cloud Private Inferenceinternet-court/internet-court-skill | 6.4k | 2 repos | ~1.3k | Automated safety check: Pass | Custom licence | |
| Nemoclaw Maintainer Normalize Title TagsNVIDIA/NemoClaw | 23k | — | ~693 | Automated safety check: Pass | Apache-2.0 |
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
NVIDIA/SkillSpector
Decides whether an agent skill is safe to install by combining a SkillSpector static scan with the agent's own source review, ending in APPROVE, CAUTION or REJECT.
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
internet-court/internet-court-skill
Shows how to call NEAR AI Cloud through an OpenAI-compatible API and verify that inference ran in a TEE, using attestation checks and signed chat responses.
NVIDIA/NemoClaw
Remove bracketed NemoClaw tags from GitHub issue and PR titles.
NVIDIA/Megatron-LM
Walks an agent through working inside the Megatron-LM CI container and changing dependencies with uv, so lock files resolve the same locally and in CI.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Works with
Build and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware…. Doca Flow is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Build and debug DOCA Flow applications on supported NVIDIA NICs/DPUs: define match/action pipes, initialize ports and representors, choose forwarding targets, validate pipes before hardware programming, read counters, match the Flow version to the installed DOCA release, and diagnose Flow API errors.
Doca Flow fits situations like: DOCA packet steering; 5-tuple-to-queue questions even when DOCA Flow is not named.
Run `npx skills add NVIDIA/skills --skill doca-flow -a claude-code`. Or copy the skill folder (skills/doca-flow in NVIDIA/skills) into .claude/skills/doca-flow in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill doca-flow -a codex`. Or copy the skill folder (skills/doca-flow in NVIDIA/skills) into .agents/skills/doca-flow in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill doca-flow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/doca-flow, .gemini/skills/doca-flow, .github/skills/doca-flow and .opencode/skills/doca-flow in your project.
SKILL.md names no scripts, command-line tools or credentials: Doca Flow is instructions for the agent only. Compatibility (from SKILL.md): Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a supported NVIDIA NIC/DPU attached. Reads the user's local install via `pkg-config doca-flow` and inspects /opt/mellanox/doca/{lib,include,samples,applications}. .
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Doca Flow is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Doca Flow: LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Skill Inspector (NVIDIA/SkillSpector, 20k stars), Embeddings via 9Router (decolua/9router, 30k stars) and NEAR AI Cloud Private Inference (internet-court/internet-court-skill, 6.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.