Graphsignal
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.
A skill your agent uses to promote overlay files and built artifacts into the staged BSP image.
$ npx skills add NVIDIA/skills --skill jetson-promote-image -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills jetson-promote-image --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-promote-image .claude/skills/jetson-promote-image && 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-promote-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-promote-image into .claude/skills/jetson-promote-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-promote-image", 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-promote-imageType 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-promote-image -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills jetson-promote-image --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-promote-image .agents/skills/jetson-promote-image && 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-promote-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-promote-image into .agents/skills/jetson-promote-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-promote-image", 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-promote-image -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills jetson-promote-image --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-promote-image .cursor/skills/jetson-promote-image && 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-promote-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-promote-image into .cursor/skills/jetson-promote-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-promote-image", 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-promote-image--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-promote-image -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills jetson-promote-image --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-promote-image .gemini/skills/jetson-promote-image && 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-promote-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-promote-image into .gemini/skills/jetson-promote-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-promote-image", 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-promote-imageInstalls 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-promote-image -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-promote-image .github/skills/jetson-promote-image && 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-promote-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-promote-image into .github/skills/jetson-promote-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-promote-image", 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-promote-image -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-promote-image --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-promote-image .opencode/skills/jetson-promote-image && 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-promote-image" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-promote-image into .opencode/skills/jetson-promote-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-promote-image", 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-promote-imageA skill your agent uses to promote overlay files and built artifacts into the staged BSP image.
Jetson Promote Image is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use to promote overlay files and built artifacts into the staged BSP image. Do NOT use to flash or build. Triggers: promote bsp image.
Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/build-source-freshness-gate.md`).
It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform, Linux and Git. 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 dfdd080. 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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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 Promote Image loads about 5k tokens when it runs, and up to ~9.3k if it reads all its reference files. Until then it costs about 39 tokens; SKILL.md has 2,207 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 noted patterns worth knowing about, such as sudo or a known installer.
`rootfs/` were extracted with `sudo tar xpjf` bybits the flashing toolchain reads back. `sudo cp -p`n the host | Run on an account that can `sudo cp`; re-run resumes via diff-aware copy. |st the stale kernel | Force the gate by `sudo touch <LFT_DST>/kernel/Image` + re-run promote, or run the two steps manuaAutomated 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 dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 2,207 words, ~4,992 tokens.
.claude/skills/jetson-promote-image/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.Stage every Customize-* and Build output into bsp_image so it is
ready for /jetson-flash-image. This is the promote leg of
Deploy — it copies files, never flashes and never builds.
source: and bsp_image:
resolved (run /jetson-init-source and /jetson-init-image first).<source.root_path>/Linux_for_Tegra/ initialized as a git repo
(overlay tracker) with a clean working tree.<bsp_image.root_path>/Linux_for_Tegra/ extracted from a BSP
tarball + apply_binaries.sh already run.git, yq, cmp, and sudo (for rootfs/* destinations) on
the host.<source.root_path>/.build-manifest.yaml + .build-state.yaml
from /jetson-build-source (required when kernel-side repos
have customize-* commits).This is the promote leg of Deploy — see
../../context/bsp-customization-workflow.md
for the pipeline view. The two channels this skill walks are:
| Channel | Source | Carrier | Owner |
|---|---|---|---|
| Overlay tracker | <source.root_path>/Linux_for_Tegra/ (git repo at HEAD) | Customize-* outputs that don't require a build (e.g. nvfancontrol.conf, nvpmodel.conf, BPMP DTB hand-edits) | Customize customize-* skills commit here |
| Build manifest | <source.root_path>/.build-manifest.yaml | Rebuilt kernel Image, in-tree .ko, OOT .ko, NVIDIA DTBs | Build jetson-build-source writes here |
The skill computes the union of files to copy and writes each into
<bsp_image.root_path>/Linux_for_Tegra/ with diff-aware
skip-if-identical logic. When the copy pass touches the kernel
Image or anything under rootfs/lib/modules/, it also rebuilds
the initramfs via NVIDIA's tools/l4t_update_initrd.sh so the
freshly promoted kernel + modules ship in the initrd the
bootloader actually loads. After it returns, bsp_image carries
every Customize and Build output. The skill does not flash and
does not modify the workspace.
jetson-promote-image → jetson-flash-image → jetson-validate-image.bsp_image updated but isn't
ready to flash yet (e.g. to inspect resolved files, run an
out-of-band build that reads bsp_image, or hand bsp_image to a
separate flashing host).Resolve the active profile per the contract in
../../context/target-platform-contract.md.
Refuse and route in these cases:
| Condition | Refuse with |
|---|---|
No active profile, or active: NA | Route to /jetson-set-target or /jetson-init-target. |
Profile lacks bsp_image: | Route to /jetson-init-image. |
<bsp_image.root_path>/Linux_for_Tegra/ missing | Route to /jetson-init-image. |
<source.root_path>/Linux_for_Tegra/ missing or not a git repo | Route to /jetson-init-source. |
Resolve paths:
<workspace> = parent of the active profile's target-platform/
directory (discovered at load time).<bsp_image.root_path> from bsp_image.root_path: if present,
else <workspace>/Image.<source.root_path> from source.root_path: if present, else
<workspace>/Source.Bind shell variables for the rest of the procedure:
LFT_SRC="<source.root_path>/Linux_for_Tegra" # overlay tracker
LFT_DST="<bsp_image.root_path>/Linux_for_Tegra"
MANIFEST="<source.root_path>/.build-manifest.yaml" # build outputsThe skill needs at least one channel populated. Refuse if the
overlay tracker has uncommitted changes (status --porcelain
non-empty), if $MANIFEST exists but doesn't parse as YAML, or
if both channels are empty. Records OVERLAY_HAS_COMMITS /
OVERLAY_HEAD and MANIFEST_PRESENT for downstream steps.
See references/copy-pass-snippets.md
for the shell snippet and refuse messages.
Refuse if .build-state.yaml shows any kernel-side repo in
Source/bsp_sources/ dirty since the last /jetson-build-source
— otherwise the copy pass would silently ship stale artifacts.
Detection rules + shell snippet in
references/build-source-freshness-gate.md.
Records BUILD_FRESH=1.
When the overlay tracks a remote, refuse if upstream has commits
not yet pulled. Skip gracefully when no remote is configured
(the default git init empty tracker from jetson-init-source).
Manifest channel has no git remote concept — this check is
overlay-only. Records COLLISION_CHECK for the Summary.
See references/copy-pass-snippets.md
for the shell snippet.
Channel A — overlay: git ls-files against $LFT_SRC is
the source of truth (transparent to symlink mounts when
source.repos.Linux_for_Tegra was overridden, excludes
untracked / .gitignored files). Each entry maps
src = $LFT_SRC/<rel> → dst = $LFT_DST/<rel>.
Channel B — manifest: parse artifacts[].{src,dst} from
$MANIFEST. Refuse if any src is missing on disk (build was
interrupted, or manifest stale — re-run /jetson-build-source).
The manifest schema is written by
jetson-build-source v0.2.0.
See references/copy-pass-snippets.md
for both shell snippets and the manifest YAML schema.
Iterate the union of overlay files and manifest entries. For
each dst: if byte-identical, skip; otherwise cp -p (with
sudo for rootfs/* destinations, where the sample rootfs
was extracted as root). Tag INITRD_DIRTY=1 on any
rootfs/lib/modules/* or kernel/Image write — the
"Refresh initramfs" step gates on this flag. Counts /
FIRST / LAST are recorded for the Summary.
Fail-fast: if any cp fails, surface the failed path and stop.
bsp_image may be left partially updated — re-running after
fixing the cause resumes via the diff-aware skip. Channel
order is overlay first, then manifest: on a dst collision
the manifest wins (freshly built artifact beats the older
overlay copy).
See references/copy-pass-snippets.md
for the copy_one() function and the two driving loops.
The kernel Image lives in two paths inside bsp_image:
<LFT_DST>/kernel/Image (read by the flash tool) and
<LFT_DST>/rootfs/boot/Image (the rootfs-side copy, visible as
/boot/Image from inside the rootfs chroot the refresh tool
will run in). The build manifest only carries the kernel/Image
dst, so this step mirrors kernel/Image → rootfs/boot/Image
(diff-aware, no-op when already in sync) so the chrooted refresh
tool resolves the kernel against the freshly promoted binary,
not the stale rootfs copy. The mirror also sets INITRD_DIRTY=1
so a kernel-only promote (no rootfs/lib/modules/* writes) still
triggers the refresh.
See references/kernel-image-and-initramfs.md
for the shell snippet, the failure mode this prevents, and the
INITRD_DIRTY corner case.
Run tools/l4t_update_initrd.sh from <LFT_DST>/ whenever
INITRD_DIRTY=1 (set by the diff-aware copy or the mirror step
above). The tool chroots into rootfs/, runs NVIDIA's
nv-update-initrd, and writes both
<LFT_DST>/bootloader/l4t_initrd.img (used by the flash tool)
and <LFT_DST>/rootfs/boot/initrd (/boot/initrd on the DUT).
Idempotent; ~30 s. Skip when INITRD_DIRTY=0 (overlay-only
edits). DUT-side workarounds (update-initramfs -u + manual
cp) are out of scope — fix the gap here so flash ships a
coherent image.
See references/kernel-image-and-initramfs.md
for the shell snippet, refuse paths, the "module shadowing" and
"vermagic skew" failure modes the rebuild closes, and why
bootloader/initrd (a different file) is left alone.
Report:
overlay HEAD ($OVERLAY_HEAD) or "(empty)".mode=<...>, bsp_version=<...>, rebuilt_at=<...>, N artifacts or "(absent)".$COLLISION_CHECK.$COPIED_OVERLAY copied, $IDENTICAL_OVERLAY identical$COPIED_MANIFEST copied, $IDENTICAL_MANIFEST identical$KIMG_MIRRORED and initramfs:
$INITRD_STATUS (copied … / rebuilt when triggered by
kernel/Image or rootfs/lib/modules/* writes; skipped …
otherwise).COPIED totals are 0).<source.root_path>, <bsp_image.root_path>./jetson-flash-image (or /jetson-validate-image if
the user only wanted bsp_image refreshed for inspection / static
validation).bsp_image/Linux_for_Tegra/
is written by both passes. Overlay carries customize-* outputs
(overlay-only edits like nvfancontrol.conf); manifest carries
rebuilt binaries (kernel/OOT/DT). The two are intentionally
disjoint by construction: build outputs don't go into the
overlay, and customize-* edits to non-build files don't enter
the manifest.jetson-build-source's "Write the build manifest" step trace policy). Promoting the
manifest is therefore safe: every entry is a customization-bearing
artifact, not toolchain-divergence noise. The skill does not
re-derive the trace — it trusts the manifest.Source/.build/ or bsp_sources/'s build artifacts
between jetson-build-source and jetson-promote-image, the
manifest will reference missing files. The "Enumerate sources (both channels)" step refuses in that
case and points the user at /jetson-build-source to rebuild.customize-fan)
produces no build outputs and writes no manifest — the "Enumerate sources (both channels)" step is a
no-op, the "Diff-aware copy into bsp_image" step promotes only overlay files. The skill prints
"manifest: (absent)" in the summary and continues.source.repos.Linux_for_Tegra was overridden in
jetson-init-source, the canonical mount is a symlink into
<source.root_path>/.repos/Linux_for_Tegra/<subdir>. git -C,
cp -p, and cmp -s all follow it transparently — no special
handling needed at this layer. Manifest src paths are
absolute, so symlinks under bsp_sources/ don't matter for the
manifest channel.sudo is scoped to rootfs/ destinations. Files under
rootfs/ were extracted with sudo tar xpjf by
jetson-init-image, so they carry root ownership and special
mode bits the flashing toolchain reads back. sudo cp -p
preserves them. Everything else (bootloader/, kernel/,
kernel/dtb/, tools/, etc.) is user-owned and does not need
sudo. This applies to both channels.dst appears in
both overlay and manifest, manifest wins (later in the "Diff-aware copy into bsp_image" step's
loop). This is the desired semantic — manifest entries are
freshly built, overlay entries may be older state. Hand-editing
binary files into the overlay is discouraged (Build's job
is to rebuild them); the precedence rule makes such mistakes
recoverable.bsp_image is read-only outside Deploy. This skill is the
only writer in the normal flow (matches the workflow invariant).
Hand-edits to <bsp_image.root_path>/Linux_for_Tegra/ outside
Deploy will be silently overwritten on the next promote run if
the same path exists in either channel; conversely they will
not be reverted if no entry shadows them. Both behaviors are
wrong for the diff trail — never hand-edit upstream.git -C $LFT_SRC checkout <ref> first,
then re-run. The manifest channel has no ranged scope — it
reflects whatever jetson-build-source's last run produced.cp fails
partway through, bsp_image is left in an intermediate state.
Fix the underlying cause (usually permissions / disk full) and
re-run — the "Diff-aware copy into bsp_image" step will resume by skipping already-promoted files.Image mirror + initramfs refresh. Gated on copy-pass
writes to kernel/Image or rootfs/lib/modules/*; the mirror
feeds the refresh's chroot. Both are diff-aware and skipped on
pure-overlay edits. tools/l4t_update_initrd.sh must exist in
bsp_image (ships with apply_binaries.sh); a missing tool
refuses and routes to /jetson-init-image. See
references/kernel-image-and-initramfs.md
for the full contract and failure modes.| Error | Cause | Solution |
|---|---|---|
Overlay has uncommitted changes at <LFT_SRC> | Customize-* edits not committed before promote | Run git -C $LFT_SRC commit (or stash), then re-run. |
origin has N unpulled commits on <upstream> | Remote overlay diverged from local | git -C $LFT_SRC pull, resolve conflicts, then re-run. |
Both overlay and manifest are empty — nothing to promote | No Customize-* commits and no Build manifest | Run a customize-* skill or /jetson-build-source first. |
Kernel-side source(s) changed since last /jetson-build-source | Freshness gate detected unprocessed customize-* edits under Source/bsp_sources/ | Commit pending edits, run /jetson-build-source, re-run promote. |
Manifest entry references missing build output: <src> | bsp_sources/ build outputs wiped or stale manifest | Re-run /jetson-build-source to regenerate. |
Build manifest at <MANIFEST> is not valid YAML | Manifest hand-edited or partially written | Re-run /jetson-build-source to rewrite the manifest. |
cp: permission denied under rootfs/ | Missing sudo privilege on the host | Run on an account that can sudo cp; re-run resumes via diff-aware copy. |
Profile lacks bsp_image: / source: | Workspace not bootstrapped | Run /jetson-init-image and/or /jetson-init-source. |
tool not found at <LFT_DST>/tools/l4t_update_initrd.sh | tools/ was pruned, or bsp_image extracted from a non-NVIDIA tarball | Re-run /jetson-init-image to repopulate. |
l4t_update_initrd.sh exited non-zero | Insufficient sudo, broken rootfs (missing lib/modules/<ver>/modules.dep), or out-of-space /tmp | Run depmod -a -b <LFT_DST>/rootfs <ver> against the rootfs first; verify /tmp headroom; rerun promote. |
DUT boots with stale kernel / modules after promote, modules fail to load with disagrees about version of symbol …, or initramfs ships pre-customize modules even after the refresh ran | The mirror / refresh gate didn't fire (manual hand-edit under <LFT_DST> outside the skill), or rootfs/boot/Image drifted from kernel/Image so the chrooted refresh built against the stale kernel | Force the gate by sudo touch <LFT_DST>/kernel/Image + re-run promote, or run the two steps manually: sudo cp -p <LFT_DST>/kernel/Image <LFT_DST>/rootfs/boot/Image && cd <LFT_DST> && sudo ./tools/l4t_update_initrd.sh. Then re-flash. See references/kernel-image-and-initramfs.md. |
Locked in for v0.2.0:
<bsp_image.root_path>/Linux_for_Tegra/.dst collision.bsp_sources/ are not
fetched (their state was sealed when jetson-build-source
wrote the manifest).kernel/Image →
rootfs/boot/Image whenever the copy pass touched
kernel/Image; the refresh runs
tools/l4t_update_initrd.sh whenever kernel/Image or any
rootfs/lib/modules/* was promoted, rebuilding both
bootloader/l4t_initrd.img and rootfs/boot/initrd.
Inseparable because the refresh chroots into rootfs/ and
resolves the kernel through /boot/Image — the mirror has to
run first. Closes both module-shadowing and vermagic-skew
failure modes; both diff-aware, both skipped on overlay-only
edits. Full contract in
references/kernel-image-and-initramfs.md.Still deferred:
/jetson-build-source at
the prior commit. A manifest archive (saved per-build-mode or
per-commit) would enable rollback without rebuild.bsp_image. Revisit when promotion
happens on a host that does not have access to the overlay
tracker repo (or the workspace's manifest file).references/kernel-image-and-initramfs.md — full contract for the kernel Image mirror + l4t_update_initrd.sh refresh: shell snippets, failure modes, tool semantics, output filenames.../../context/target-platform-contract.md — target-platform contract.../../context/bsp-customization-workflow.md — workspace edit protocol (this skill is the promote leg of Deploy).../jetson-init-source/SKILL.md — Setup; materializes the overlay tracker this skill reads (channel A) and authors source.toolchain.../jetson-build-source/SKILL.md — Build builder; writes the .build-manifest.yaml this skill reads (channel B).../jetson-flash-image/SKILL.md — next leg; flashes the just-promoted bsp_image to the DUT.../jetson-validate-image/SKILL.md — final leg; static + on-target validation.© 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 (references) in skills/jetson-promote-image of NVIDIA/skills.
Open the folder on GitHubat commit dfdd080
Jetson Promote Image 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 Promote Image this skillNVIDIA/skills | 3.5k | — | ~5k | Automated safety check: Notes | Apache-2.0 | |
| Graphsignalgraphsignal/graphsignal | 257 | — | ~6.2k | Automated safety check: Pass | Apache-2.0 | |
| LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS | 925 | — | ~2.8k | Automated safety check: Pass | None | |
| Cv DeployLMIXR/CV_Deployment_skill | 170 | — | ~547 | Automated safety check: Pass | None | |
| TensorRT-LLM InferenceOrchestra-Research/AI-Research-SKILLs | 13k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Triton SkillslowlyC/agent-gpu-skills | 169 | — | ~1.3k | Automated safety check: Pass | MIT |
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.
BBuf/AI-Infra-Auto-Driven-SKILLS
Analyzes Torch Profiler traces from SGLang, vLLM and TensorRT-LLM servers into kernel attribution, overlap and fusion tables.
LMIXR/CV_Deployment_skill
基于 helpfile 工程经验,协助 agent 配置 CV 主机和边缘设备环境、编译视觉与推理依赖、接入摄像头视频并打包部署服务。适用于 Ubuntu、CentOS、Windows、macOS、Jetson、树莓派和 RK3399 的 CV 工程实施与故障排查,以及相关移动端配套工具;模型训练和纯算法设计不属于本技能主线。
Orchestra-Research/AI-Research-SKILLs
Optimizes and serves LLMs on NVIDIA GPUs with TensorRT-LLM, covering quantization, in-flight batching, multi-GPU parallelism and the trtllm-serve command.
slowlyC/agent-gpu-skills
Write, debug, and optimize Triton and Gluon GPU kernels from local upstream tutorials, production kernels, language definitions, and compiler source.
Orchestra-Research/AI-Research-SKILLs
Sets up large-scale LLM training with NVIDIA Megatron-Core, choosing tensor, pipeline, data, context and expert parallelism for a given model size and GPU count.
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
A skill your agent uses to promote overlay files and built artifacts into the staged BSP image. Jetson Promote Image is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use to promote overlay files and built artifacts into the staged BSP image.
Jetson Promote Image fits situations like: promote overlay files and built artifacts into the staged BSP image; tasks that involve GPU and accelerator computing.
Run `npx skills add NVIDIA/skills --skill jetson-promote-image -a claude-code`. Or copy the skill folder (skills/jetson-promote-image in NVIDIA/skills) into .claude/skills/jetson-promote-image in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill jetson-promote-image -a codex`. Or copy the skill folder (skills/jetson-promote-image in NVIDIA/skills) into .agents/skills/jetson-promote-image 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-promote-image -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-promote-image, .gemini/skills/jetson-promote-image, .github/skills/jetson-promote-image and .opencode/skills/jetson-promote-image in your project.
Going by SKILL.md and its folder, Jetson Promote Image needs the command-line tools its instructions call (git).
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Jetson Promote Image 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 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Jetson Promote Image: Graphsignal (graphsignal/graphsignal, 257 stars), LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 925 stars), Cv Deploy (LMIXR/CV_Deployment_skill, 170 stars) and TensorRT-LLM Inference (Orchestra-Research/AI-Research-SKILLs, 13k 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 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 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.