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 alwaysai-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/alwaysai-setup)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/alwaysai-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/alwaysai-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/alwaysai-setup"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/alwaysai-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, 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 29 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 63 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 MCP Rug Pull · line 68 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium Rogue Agent · line 136 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Privilege Escalation · line 135 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 136 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 | $0.00048 | $0.00990 |
| Opus 5.5 | $0.00019 | $0.00396 |
| Sonnet 5.5 | $0.00010 | $0.00198 |
| Haiku 4.5 | $0.00005 | $0.00099 |
Grade B, and why
alwaysai-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 apt-cache show nvidia-jetpack How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
alwaysAI on NVIDIA Jetson
alwaysAI is a computer vision development platform for creating and deploying ML applications on edge devices. Deploy object detection projects from a host PC to Jetson via SSH, with TensorRT-optimized models for real-time inference.
Hardware: Jetson device (Nano/Xavier NX/AGX Xavier/AGX Orin), USB webcam or MIPI CSI camera Software: JetPack 4.6 with all SDK components, host PC (Windows/Linux/Mac)
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)
On Jetson:
sudo apt-cache show nvidia-jetpack
# Confirm JetPack 4.6
ls /dev/video*
# Confirm camera is connected
[OK] when JetPack 4.6 confirmed and camera detected.
Phase 2 — setup host PC (~5 min)
On the development PC:
- Download and install alwaysAI from https://alwaysai.co/installer/windows (or Mac/Linux equivalent)
- Verify CLI:
aai -v
- Verify OpenSSH:
ssh -V
[OK] when aai and ssh both return version numbers.
Phase 3 — setup Jetson environment (~2 min)
On Jetson:
sudo usermod -aG docker $USER
Log out and back in, then verify:
docker run hello-world
[OK] when hello-world runs without sudo.
Phase 4 — create account & project (human action)
- Sign up at https://console.alwaysai.co/auth?register=true
- Create a new project: Dashboard → New Project → Object Detection
- Delete the default
mobilenet_ssdmodel (not optimized for Jetson) - Add optimized model: Model Catalog → search
ssd_mobilenet_v1_coco_2018_01_28_xavier_nx→ Add To Project
[OK] when project has the TensorRT-optimized model.
Phase 5 — deploy to Jetson (~5 min)
On host PC, create a project folder and configure:
mkdir ~/alwaysai-project && cd ~/alwaysai-project
aai app configure
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 · 146 lines · 48 tokens per session scan B 3d54ff1bb560
alwaysai-setup is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (56 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 990 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.
Other skills, from other repositories
spark-environment-setup
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
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.
inference-serving-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.
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.
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'…