GPU Optimizer
Mathews-Tom/armory
GPU optimization for consumer NVIDIA GPUs (8-24GB VRAM) covering mixed precision, gradient checkpointing, XGBoost GPU, CuPy/cuDF migration, and torch.compile.
Bind pre-downloaded Jetson reference docs (developer guide, design guide, pinmux, schematics) into the active profile documents block.
$ npx skills add NVIDIA/skills --skill jetson-link-docs -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills jetson-link-docs --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/jetson-link-docs .claude/skills/jetson-link-docs && 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 "jetson-link-docs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-link-docs into .claude/skills/jetson-link-docs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-link-docs", 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/jetson-link-docsType 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 jetson-link-docs -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills jetson-link-docs --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/jetson-link-docs .agents/skills/jetson-link-docs && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jetson-link-docs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-link-docs into .agents/skills/jetson-link-docs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-link-docs", 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 jetson-link-docs -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills jetson-link-docs --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/jetson-link-docs .cursor/skills/jetson-link-docs && 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 "jetson-link-docs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-link-docs into .cursor/skills/jetson-link-docs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-link-docs", 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/jetson-link-docs--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 jetson-link-docs -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills jetson-link-docs --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/jetson-link-docs .gemini/skills/jetson-link-docs && 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 "jetson-link-docs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-link-docs into .gemini/skills/jetson-link-docs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-link-docs", 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 jetson-link-docsInstalls 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 jetson-link-docs -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/jetson-link-docs .github/skills/jetson-link-docs && 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 "jetson-link-docs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-link-docs into .github/skills/jetson-link-docs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-link-docs", 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 jetson-link-docs -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 jetson-link-docs --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/jetson-link-docs .opencode/skills/jetson-link-docs && 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 "jetson-link-docs" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-link-docs into .opencode/skills/jetson-link-docs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-link-docs", 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.
jetson-link-docsBind pre-downloaded Jetson reference docs (developer guide, design guide, pinmux, schematics) into the active profile documents block.
Jetson Link Docs is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Bind pre-downloaded Jetson reference docs (developer guide, design guide, pinmux, schematics) into the active profile documents block. Use after staging docs on disk; not for downloading.
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).
It sits in AI & LLM Engineering, covering GPU and accelerator computing. 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.
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.
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.
Jetson Link Docs loads about 3.3k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 1,530 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). 1,530 words, ~3,329 tokens.
.claude/skills/jetson-link-docs/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.This skill writes the documents: block of the active Jetson /
IGX target-platform profile YAML so downstream skills
(/jetson-generate-kb, /jetson-customize-pinmux, camera / pcie /
uphy, etc.) can resolve doc paths by name. It walks the user through
every document slot in the profile schema, tries to auto-bind each
slot to a file under <documents.root_path>/ via case-insensitive
glob matching, and writes the resulting paths back into the active
profile.
Scope is registering pointers only — this skill does not fetch
or download. The files must already exist on disk under
<documents.root_path>/.
/jetson-init-target finishes and the user has documents
on disk to register.jetson-generate-kb) reports "no documents
recorded" and the user wants to fix that.Resolve the active profile + <workspace> per the contract in
../../context/target-platform-contract.md.
Cache the loaded profile in memory — this skill mutates it in
the "Write the documents: block back to the profile" step.
Load
../../references/platform_template.yaml.
Parse the documents: block. Each per-document field is marked
<OPTIONAL: description>. Use the marker description as prompt text
verbatim. Match markers with the regex
^<(REQUIRED|OPTIONAL|DERIVED):\s*(.*)>$ after YAML parsing strips
surrounding quotes.
Skip custom_carrier_schematic and custom_carrier_pinmux_xls
entirely when the active profile has no custom_carrier: block —
both are meaningless without one. This filter applies through the "Scan and auto-match" and "Manual prompts for unmatched fields" steps.
documents.root_pathDefault: <workspace>/Documents. If the profile already records
documents.root_path, use it. Otherwise, if <workspace>/Documents/
exists, use it (the field is omitted from the written profile —
downstream skills fall back to the workspace default). If neither is
available, prompt the user for an absolute path, or accept Enter /
cancel to skip the auto-scan. A user-provided path that doesn't
exist is treated as skipped (warn, don't refuse — the field is
OPTIONAL); manual prompts in the "Manual prompts for unmatched fields" step still run.
Read the Product Token column from
../../references/bsp-platforms-catalogue.md
for the row matching reference_devkit.name. The token is a
case-insensitive glob fragment (e.g. *orin*nano*, *agx*thor*)
consumed by the fallback patterns in the "Scan and auto-match" step.
If reference_devkit.name has no row in the catalogue, log a warning
and proceed without a product-token fallback — the "Scan and auto-match" step still works
with strictly SKU-keyed matching.
For custom carriers, derive <custom-token> from custom_carrier.name
using this recipe: lowercase, replace each space with *, wrap in
* on both ends. E.g. "Acme Vision X1" → *acme*vision*x1*.
Skip this step entirely if documents.root_path did not resolve in
the "Resolve documents.root_path" step (no scan target → no auto-suggest; fall through to manual
prompts in the "Manual prompts for unmatched fields" step).
Scan the directory once (one level deep) and try to auto-match each
remaining <OPTIONAL:…> field using the case-insensitive globs below.
Use the lower-case module.id / carrier.id / custom_carrier.id
strings from the profile in the SKU column.
| Field | SKU glob (primary) | Product-token glob (fallback) |
|---|---|---|
bsp_developer_guide | *developer*guide*.pdf, *BSP*guide*.pdf | (no fallback — pattern is product-agnostic) |
soc_tech_ref_manual | *TRM*.pdf, *tech*ref*manual*.pdf | (no fallback — same) |
module_data_sheet | *<module.id>*data*sheet*.pdf, *<module.id>*datasheet*.pdf | <token>data*sheet*.pdf, <token>datasheet*.pdf |
module_design_guide | *<module.id>*design*guide*.pdf, *<module.id>*PDG*.pdf | <token>design*guide*.pdf, <token>PDG*.pdf |
module_thermal_design_guide | *<module.id>*thermal*.pdf (covers "Thermal Design Guide" / "TDG") | <token>thermal*.pdf |
module_schematic | *<module.id>*schem*.pdf | <token>schem*.pdf |
carrier_board_spec | *<carrier.id>*board*spec*.pdf, *<carrier.id>*spec*.pdf | <token>carrier*spec*.pdf |
carrier_schematic | *<carrier.id>*schem*.pdf | <token>carrier*schem*.pdf |
custom_carrier_schematic | *<custom_carrier.id>*schem*.pdf (only if custom carrier) | <custom-token>schem*.pdf (only if custom carrier) |
ref_devkit_pinmux_xls | *<carrier.id>*pinmux*.xls* (matches .xls, .xlsx, .xlsm) | <token>pinmux*.xls* |
custom_carrier_pinmux_xls | *<custom_carrier.id>*pinmux*.xls* (only if custom carrier) | <custom-token>pinmux*.xls* (only if custom carrier) |
<token> is the catalogue-resolved product token; <custom-token>
is derived from custom_carrier.name per the "Resolve the product token" step. Tokens already
include leading/trailing *, so the table does not repeat them.
For each field that has auto-match results:
use this? (yes/no, default yes). On yes, record it and skip
the manual prompt for that field. On no, fall through to the
manual prompt in the "Manual prompts for unmatched fields" step.skip / NA option. Never silently bind a multi-hit candidate.If documents.root_path is folder-organised one level deeper than
flat (NVIDIA archives often are: Schematics/, Design-Guides/,
Pinmux/, etc.), the file globs may return zero hits even when the
right documents exist. v0.2 only scans one level deep — when 0 hits
is suspicious (documents.root_path exists but no fields auto-bound),
surface the limitation to the user and offer to fall through to
manual prompts.
For every field that wasn't auto-bound (and wasn't filtered out in
the "Load the document-slot schema" step), prompt using the marker description from the "Load the document-slot schema" step as prompt
text, in document order. Accept Enter and NA interchangeably as
"skip this field". When the "Match policy per field" step produced 2+ candidate hits for a
field, present them as a numbered list with a skip / NA option
rather than asking for a free-text path.
Validate that user-provided paths exist on disk (warn if not, but do
not refuse — the user may be recording a planned path). URLs (values
starting with http://, https://, or ftp://) are accepted
verbatim and not validated.
documents: block back to the profileEdit target-platform/<active>.yaml in place. Preserve all other
top-level blocks (reference_devkit:, custom_carrier:,
bsp_image:, source:) and their comments verbatim. Write only the
fields the user provided — omit skipped / NA fields entirely (no
NA placeholders, no empty keys).
Edge behavior: when every field was skipped (including
documents.root_path), drop the documents: block entirely from
the profile — never write documents: {} or a block of NA values.
When only documents.root_path was provided (no per-document
binding), record it alone — the path has value as a hint for future
re-runs. On re-run with an existing documents: block, merge:
existing bindings are preserved unless the user picks a new file or
NA; newly bound fields are added.
Print a summary:
documents.root_path — resolved value (or "default — omitted").jetson-generate-kb re-reads documents.* and
should be re-run if a KB exists.If a downstream skill triggered this run, tell the user to re-issue their original request; do not silently re-trigger it.
jetson-set-target / jetson-init-target.*orin*nano* matches both Orin-Nano-specific docs and combined
Orin-NX/Nano docs (e.g. Jetson-Orin-NX-Nano-Design-Guide_…). That
is usually correct for module-side docs (NVIDIA ships combined
manuals), but verify on schematic / pinmux / spec fields where
wrong-product binding is costly.bsp-platforms-catalogue.md when adding new product
rows. The Product Token column is consumed by the "Resolve the product token" step; a
missing token degrades the auto-scan to SKU-only matching (the
skill warns and continues, but doc-rich documents.root_path
scans will degrade silently from "5 auto-binds" to "fewer auto-
binds").documents: block back to the profile" step mutates an existing
YAML file. Plain yaml.safe_load + yaml.safe_dump loses comments,
block ordering, and quoting style — use ruamel.yaml or
equivalent so hand-edited fields and comments survive.../../context/target-platform-contract.md.documents.root_path,
under default <workspace>/Documents/, or as user-provided paths /
URLs during manual prompts. A missing root only disables auto-scan; it
is not a hard prerequisite.ruamel.yaml or another round-tripping YAML writer for the profile
edit step.../../references/platform_template.yaml — no ad-hoc keys.documents.root_path will under-bind and require manual selection.documents.root_path missing — auto-scan is skipped. Provide an
absolute root path, enter individual document paths / URLs manually,
or skip the fields you do not want to bind.Jetson-Linux-Developer-Guide*.pdf files → keep the active version,
rename the stale one.ruamel.yaml and rerun against a fresh
pristine copy.documents.root_path — documents.* are relative paths only; move
the file under the root and retry.../../context/target-platform-contract.md — target-platform contract; this skill consumes and mutates the active profile.../../references/bsp-platforms-catalogue.md — source of the Product Token column for the "Resolve the product token" step.../../references/platform_template.yaml — schema for the documents: block (source of truth for prompts and field list).../jetson-init-target/SKILL.md — sibling skill that authors target identity (reference_devkit:, optional custom_carrier:).../jetson-init-image/SKILL.md — sibling skill that authors bsp_image:.../jetson-init-source/SKILL.md — sibling skill: clones shared repos and handles source.root_path overrides.../jetson-generate-kb/SKILL.md — sibling skill: consumes the documents: block this skill writes.© 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 4 other files in skills/jetson-link-docs of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
Jetson Link Docs 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 |
|---|---|---|---|---|---|---|
| Jetson Link Docs this skillNVIDIA/skills | 3.5k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| GPU OptimizerMathews-Tom/armory | 327 | — | ~3.5k | Automated safety check: Notes | MIT | |
| Fla Triton To Gluonfla-org/flash-linear-attention | 5.8k | — | ~4.2k | Automated safety check: Pass | MIT | |
| DGX Spark Memory and Thermal Opswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Graphsignalgraphsignal/graphsignal | 257 | — | ~6.2k | Automated safety check: Pass | Apache-2.0 | |
| DGX Spark Training Gotchaswshobson/agents | 40k | 1 repos | ~2k | Automated safety check: Pass | MIT |
Mathews-Tom/armory
GPU optimization for consumer NVIDIA GPUs (8-24GB VRAM) covering mixed precision, gradient checkpointing, XGBoost GPU, CuPy/cuDF migration, and torch.compile.
fla-org/flash-linear-attention
Workflow for porting an existing Triton kernel in fla/ops/ to Gluon (triton.experimental.gluon) to gain explicit control over tensor layouts, shared memory, async data movement (cp.async / TMA), MMA…
wshobson/agents
Plans memory headroom, works through out-of-memory failures and watches temperature and power during long ML training jobs on NVIDIA DGX Spark.
graphsignal/graphsignal
Profile AI inference workloads (vLLM, SGLang, TensorRT-LLM, PyTorch, any GPU application) with the Graphsignal profiler and read the results from its local /signals JSON endpoint.
wshobson/agents
Preflight checks and diagnosis for ten known failure modes of ML training on NVIDIA DGX Spark's GB10, spanning launch errors, memory, thermals, bandwidth and precision.
BBuf/AI-Infra-Auto-Driven-SKILLS
Analyzes Torch Profiler traces from SGLang, vLLM and TensorRT-LLM servers into kernel attribution, overlap and fusion tables.
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
Categories
Bind pre-downloaded Jetson reference docs (developer guide, design guide, pinmux, schematics) into the active profile documents block. Jetson Link Docs is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Bind pre-downloaded Jetson reference docs (developer guide, design guide, pinmux, schematics) into the active profile documents block.
Jetson Link Docs fits situations like: tasks that involve GPU and accelerator computing.
Run `npx skills add NVIDIA/skills --skill jetson-link-docs -a claude-code`. Or copy the skill folder (skills/jetson-link-docs in NVIDIA/skills) into .claude/skills/jetson-link-docs in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill jetson-link-docs -a codex`. Or copy the skill folder (skills/jetson-link-docs in NVIDIA/skills) into .agents/skills/jetson-link-docs 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 jetson-link-docs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jetson-link-docs, .gemini/skills/jetson-link-docs, .github/skills/jetson-link-docs and .opencode/skills/jetson-link-docs in your project.
SKILL.md names no scripts, command-line tools or credentials: Jetson Link Docs is instructions for the agent only.
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.
Jetson Link Docs 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 3.3k tokens (SKILL.md is roughly 13k 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 Jetson Link Docs: GPU Optimizer (Mathews-Tom/armory, 327 stars), Fla Triton To Gluon (fla-org/flash-linear-attention, 5.8k stars), DGX Spark Memory and Thermal Ops (wshobson/agents, 40k stars) and Graphsignal (graphsignal/graphsignal, 257 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.