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 agentmods add skills/seeed-projects/seeed-jetson-developtool/jetson-docker-setupnpx skills add Seeed-Projects/Seeed-Jetson-DevelopTool --skill jetson-docker-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/jetson-docker-setup)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/jetson-docker-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/jetson-docker-setup.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00052 | $0.01091 |
| Opus 5 | $0.00026 | $0.00545 |
| Sonnet 5 | $0.00010 | $0.00218 |
| Haiku 4.5 | $0.00005 | $0.00109 |
Grade D, and why
jetson-docker-setup scanned grade D with 3 findings 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 5d 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 docker version Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
&& rm -rf /var/cache/apk/* Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/sbsa/cuda-ubuntu1804.pin How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Getting Started with Docker on Jetson
Set up and use Docker on Seeed reComputer Jetson devices. Docker comes pre-installed on reComputer. This skill covers verification, Docker Compose installation, CUDA testing, and GPU-enabled container workflows.
Hardware: reComputer J1020 (Jetson Nano) or compatible Jetson device Software: JetPack 4.6+ (Docker pre-installed)
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 — verify Docker installation (~30 s)
sudo docker version
Expected: Docker Engine 20.10+ with OS/Arch: linux/arm64.
docker info | grep -i runtime
Expected: nvidia should appear in the runtimes list.
[OK] when Docker is running and nvidia runtime is available.
[STOP] if Docker is not installed or nvidia runtime missing.
Phase 2 — verify CUDA (~1 min)
cd /usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery
Expected: Result = PASS with device info (e.g. "NVIDIA Tegra X1").
[OK] when CUDA deviceQuery passes.
Phase 3 — install Docker Compose (~2 min)
export DOCKER_COMPOSE_VERSION=2.6.0
sudo apt-get install -y libhdf5-dev libssl-dev
sudo pip3 install docker-compose=="${DOCKER_COMPOSE_VERSION}"
If pip3 is not available:
sudo apt-get install -y python3 python3-pip
pip install docker-compose
[OK] when docker-compose --version returns successfully.
Phase 4 — install CUDA toolkit (optional, ~5 min)
Only needed if upgrading CUDA beyond the pre-installed version:
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/sbsa/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda-repo-ubuntu1804-11-3-local_11.3.1-465.19.01-1_arm64.deb
sudo dpkg -i cuda-repo-ubuntu1804-11-3-local_11.3.1-465.19.01-1_arm64.deb
sudo apt-key add /var/cuda-repo-ubuntu1804-11-3-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda
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.
- 5d ago First seen · 141 lines · 52 tokens per session scan D 14bb73ab75c8
jetson-docker-setup is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,091 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it D with 3 findings (asks for root, recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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