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/hardhat-setupnpx skills add Seeed-Projects/Seeed-Jetson-DevelopTool --skill hardhat-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/hardhat-setup)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/hardhat-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/hardhat-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 | $0.00064 | $0.01157 |
| Opus 5 | $0.00032 | $0.00579 |
| Sonnet 5 | $0.00013 | $0.00231 |
| Haiku 4.5 | $0.00006 | $0.00116 |
Grade D, and why
hardhat-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 4d 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 apt-get install libatlas-base-dev libportaudio2 libportaudiocpp0 portaudio19-dev Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hard Hat Detection with Edge Impulse on Jetson
Execution model
Run one phase at a time. After each phase:
- Relay all command output to the user.
- If output contains
[STOP]→ stop immediately, consult the failure decision tree below. - If output ends with
[OK]→ tell the user "Phase N complete" and proceed to the next phase.
Prerequisites
| Requirement | Details |
|---|---|
| Hardware | NVIDIA Jetson Nano, Xavier NX, or Xavier AGX |
| Peripherals | USB camera, HDMI display, keyboard, mouse |
| Account | Edge Impulse account (https://studio.edgeimpulse.com) |
| Network | Internet connection on both PC and Jetson |
| Software | Ubuntu on Jetson, Edge Impulse CLI |
Phase 1 — Create Edge Impulse project (~2 min)
- Register/login at https://studio.edgeimpulse.com
- Click "Create new project", name it "Hard hat detection"
- Select "Image" as the data type
- Set configuration to "Classify multiple objects (object detection)"
[OK] when the project dashboard is visible.
Phase 2 — Collect and label data (~15–30 min)
Choose one of three data collection methods:
Option A — Upload public datasets:
- Download from Flickr-Faces-HQ Dataset (https://github.com/NVlabs/ffhq-dataset)
- Upload via "Data acquisition" → "Upload data" in Edge Impulse
Option B — PC camera:
- From Dashboard, click "LET'S COLLECT SOME DATA" → select computer
- Grant camera access, capture images
- Label as "Hard Hat" and "Head"
Option C — Jetson camera:
- Connect Jetson to Edge Impulse:
ping -c 3 www.google.com
edge-impulse-linux
- Select USB camera, name the device
- Capture and label images from the "Data acquisition" page
After collection, go to "Labeling queue" and draw bounding boxes around heads. Label as "Hard Hat" or "Head".
[OK] when labeled data appears in Data acquisition. [STOP] if Jetson can't connect to Edge Impulse.
Phase 3 — Train the model (~10–30 min)
- Go to "Impulse design" → add image processing block and object detection learning block → Save impulse
- Click "Image" → configure as "RGB" → "Save Parameters" → "Generate features"
- Click "Object detection" → "Start training"
- When training completes, click "Model testing" to evaluate
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.
- 4d ago First seen · 143 lines · 64 tokens per session scan D 558064068af5
hardhat-setup is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 1,157 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it D with 3 findings (asks for root, downloads and executes remote code, 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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