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 yolov8-trtgit 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/yolov8-trt)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/yolov8-trt"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/yolov8-trt/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/yolov8-trt"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/yolov8-trt.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 119 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 | $0.00080 | $0.01290 |
| Opus 5 | $0.00040 | $0.00645 |
| Sonnet 5 | $0.00016 | $0.00258 |
| Haiku 4.5 | $0.00008 | $0.00129 |
Grade B, and why
yolov8-trt scanned grade B with 2 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 apt install python3-pip -y Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget files.seeedstudio.com/YOLOv8-Jetson.py && python YOLOv8-Jetson.py How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy YOLOv8 with TensorRT on Jetson
Deploy YOLOv8 models on Jetson with TensorRT acceleration for detection, segmentation, classification, pose estimation, and tracking. Includes one-line setup, pre-trained models, and custom model training workflows.
Execution model
Run one phase at a time. After each phase:
- Relay all output to the user.
- If output contains
[STOP]→ stop immediately, consult the failure decision tree. - If output ends with
[OK]→ tell the user "Phase N complete" and proceed to the next phase.
Prerequisites
| Requirement | Detail |
|---|---|
| Jetson device | reComputer J4012 or any Jetson with JetPack 5.1.1+ |
| JetPack | 5.1.1 or higher |
| Host PC | Ubuntu (native or VM) for flashing if needed |
| Network | Internet access for downloading models and packages |
Phase 1 — One-line YOLOv8 deployment (~10 min)
The fastest way to get started — this script installs all dependencies and downloads pre-trained models:
wget files.seeedstudio.com/YOLOv8-Jetson.py && python YOLOv8-Jetson.py
[OK] when the script completes and YOLOv8 is ready to use. [STOP] if download fails or dependency errors occur.
Phase 2 — Run pre-trained models (PyTorch)
Object detection:
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' show=True
Image classification:
yolo classify predict model=yolov8n-cls.pt source='https://ultralytics.com/images/bus.jpg' show=True
Image segmentation:
yolo segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg' show=True
Pose estimation:
yolo pose predict model=yolov8n-pose.pt source='https://ultralytics.com/images/bus.jpg'
Object tracking (on video):
yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc"
For webcam input, replace source= with source='0'. Add device=0 if errors occur.
[OK] when predictions display correctly. [STOP] if model download or inference fails.
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 · 154 lines · 80 tokens per session scan B ec61abb7ba19
yolov8-trt is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed today), licensed MIT. It adds 80 tokens to every session and 1,290 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
llama-cpp
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
minicpm5-deploy-vllm-ascend
Deploy MiniCPM5-2B with vLLM on Huawei Ascend NPU using vLLM-Ascend. Use when the user mentions vLLM-Ascend, Ascend NPU, Huawei Ascend, CANN, torchnpu, davinci devices, or wants an OpenAI-compatible MiniCPM5 server on Ascend hardware.
amc-run-rtsp-calibration
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.