Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Seeed-Projects/Seeed-Jetson-DevelopTool --skill cvedia-setupgit clone --depth 1 https://github.com/Seeed-Projects/Seeed-Jetson-DevelopToolWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/cvedia-setup)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/cvedia-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/cvedia-setup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/cvedia-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/cvedia-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Privilege Escalation · line 59 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.00782 |
| Opus 5.5 | $0.00021 | $0.00313 |
| Sonnet 5.5 | $0.00010 | $0.00156 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade B, and why
cvedia-setup scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo ./install.sh How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CVEDIA-RT on NVIDIA Jetson
CVEDIA-RT is a modular, cross-platform AI inference engine with pre-loaded applications: crowd estimation, drone detection, fall detection, lane occupancy, vehicle type counter, package detection, and more.
Hardware: NVIDIA Jetson device (tested with reComputer J4012) Software: JetPack 5.1 with all SDK components, internet connection Account: CVEDIA (free sign-up at rt.cvedia.com)
Execution model
Run one phase at a time. After each phase:
- If output contains
[STOP]→ stop immediately, consult the failure decision tree - If output ends with
[OK]→ tell the user "Phase N complete" and proceed
Phase 1 — prerequisites check (~30 s)
cat /etc/nv_tegra_release
# Expected: R35 (release) for JetPack 5.x
dpkg -l | grep nvidia-jetpack
[OK] when JetPack 5.1+ with SDK components confirmed.
[STOP] if JetPack version is incompatible.
Phase 2 — download CVEDIA-RT (human action + ~5 min)
- Visit https://rt.cvedia.com/ and sign in (or create account)
- Click Download under "NVIDIA Jetson"
- Select "Docker (Recommended)" to download the tar.gz installer
Transfer the file to the Jetson device.
[OK] when tar.gz file is on the Jetson.
Phase 3 — install CVEDIA-RT (~3 min)
mkdir -p ~/cvedia && cd ~/cvedia
tar -xzvf <filename.tar.gz>
sudo ./install.sh
Respond to installer prompts as needed.
[OK] when installation completes without errors.
Phase 4 — run CVEDIA-RT (~1 min)
With internet (first run, downloads models):
./run.sh
Without internet (after first online run):
./run.sh -U
Expected: CVEDIA-RT application opens with pre-loaded solutions.
[OK] when the application UI is visible.
Phase 5 — explore applications (~5 min)
- Click on a solution category (e.g. intelligent-transportation-systems)
- Click the run button next to a solution (e.g. lane-occupancy)
- Wait for model and video files to download
- Configure video source: gear icon → Edit Source → select camera/file → Save Instance
- Stop and restart the application for changes to take effect
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 113 lines · 52 tokens per session scan B 5b2661a5b039
cvedia-setup is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (56 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 782 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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deepstream-generate-pipeline
Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline'…