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 ultralytics-yolo-jetson-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/ultralytics-yolo-jetson-setup)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/ultralytics-yolo-jetson-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/ultralytics-yolo-jetson-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/ultralytics-yolo-jetson-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/ultralytics-yolo-jetson-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to high
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 →
- high Tool Misuse · line 69 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 88 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- high Tool Misuse · line 149 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- medium Privilege Escalation · line 79 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.
- medium Privilege Escalation · line 88 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.
- medium Privilege Escalation · line 149 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.
- medium Privilege Escalation · line 83 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 | $0.00112 | $0.01702 |
| Opus 5 | $0.00056 | $0.00851 |
| Sonnet 5 | $0.00022 | $0.00340 |
| Haiku 4.5 | $0.00011 | $0.00170 |
Grade B, and why
ultralytics-jetson-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 6d 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.
if ! id -nG | grep -qw video; then sudo usermod -aG video $USER; fi Copies of this mod
1 near-identical copy found in the catalogue:
- yolov26-jetson-setup — 88% identical, 94 lines differ
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ultralytics YOLO Jetson Setup (YOLOv8 / YOLOv11 / YOLOv26)
Execution model
Run the install script one phase at a time. After each phase:
- Relay the output to the user (show
[install]lines) - If it printed
[STOP]→ stop, follow the failure decision tree below - If it ended with
[install] Phase ... done.or[OK]→ proceed to next phase
Why phases? The torch/torchvision download is ~900 MB and takes 15–25 min. Running everything in one call risks silent timeout. Phases give the user visibility and a recovery point at each step.
Phase commands
Replace <ENV> with the env name the user requested (default: ultralytics).
Replace <FAMILY> with the model family: yolov8 | yolo11 | yolo26 (default: yolo11).
All phases use the same script — the script is idempotent; already-done steps
are skipped automatically.
Phase 1 — Environment + setup (fast, ~1 min)
bash ~/.agents/skills/ultralytics-jetson-setup/scripts/install_ultralytics.sh \
--env <ENV> --model-family <FAMILY> --phase env
Phase 2 — Download wheels (slow, 15–25 min, shows % progress)
bash ~/.agents/skills/ultralytics-jetson-setup/scripts/install_ultralytics.sh \
--env <ENV> --model-family <FAMILY> --phase download
Phase 3 — Install torch + verify CUDA (fast, ~2 min)
bash ~/.agents/skills/ultralytics-jetson-setup/scripts/install_ultralytics.sh \
--env <ENV> --model-family <FAMILY> --phase torch-pre
Phase 4 — onnxruntime / numpy / opencv + ultralytics (medium, ~8 min)
bash ~/.agents/skills/ultralytics-jetson-setup/scripts/install_ultralytics.sh \
--env <ENV> --model-family <FAMILY> --phase deps
Phase 5 — Reinstall torch + validate CUDA NMS (fast, ~3 min)
bash ~/.agents/skills/ultralytics-jetson-setup/scripts/install_ultralytics.sh \
--env <ENV> --model-family <FAMILY> --phase torch-post
Failure decision tree
| Output | Action |
|---|---|
[STOP] conda not found |
Ask user: "Please confirm miniconda is installed at ~/miniconda3 or give path" |
[STOP] conda env ... exists but has Python X.Y |
Should not happen (script auto-fixes). If seen, run conda env remove -n <ENV> then re-run phase 1 |
[STOP] torch wheel not found after download |
SharePoint URL expired. Ask user to re-share wheel files or place them manually in ~/wheels/ |
[STOP] torch.cuda.is_available() = False |
Wheel may be corrupt. Run: rm ~/wheels/*.whl then re-run phase 2 and 3 |
[FAIL] Some checks failed |
Read which line shows ✗, then check references/conflict_resolution_playbook.md for F1–F7 |
| Script hangs with no output >5 min | Report to user, ask whether to cancel and retry |
What ships with it
6 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.
- 6d ago First seen · 160 lines · 112 tokens per session scan B 4ea0d31d552a
ultralytics-jetson-setup is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 1,702 once invoked, about $0.0006 per session on Opus 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 2026-09-03.
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spark-memory-thermal-ops
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spark-training-gotchas
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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.