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 quantized-llama2-7b-mlcgit 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/quantized-llama2-7b-mlc)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/quantized-llama2-7b-mlc"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/quantized-llama2-7b-mlc.svg" alt="Measured on agentmods" 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 Privilege Escalation · line 3 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 28 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 27 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 Privilege Escalation · line 47 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 48 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 78 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 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.
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 | $0.00082 | $0.01216 |
| Opus 5 | $0.00041 | $0.00608 |
| Sonnet 5 | $0.00016 | $0.00243 |
| Haiku 4.5 | $0.00008 | $0.00122 |
Grade B, and why
quantized-llama2-7b-mlc 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 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-get update 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.
Quantized Llama2-7B with MLC LLM 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 | Minimum |
|---|---|
| Hardware | reComputer J4012 (Jetson Orin NX 16GB) or equivalent |
| RAM | ≥ 16 GB |
| JetPack | 5.x (R35.x) |
| Storage | SSD recommended — model weights + Docker images are large |
| Internet | Required for Docker pull and model download |
| HuggingFace | Access token with Llama2 model access granted |
Phase 1 — Preflight
cat /etc/nv_tegra_release
free -h
df -h /
Expected: R35.x (JP5), ≥16 GB RAM, ≥50 GB disk free. [OK] when all pass. [STOP] if insufficient RAM or disk.
Phase 2 — Install dependencies and clone jetson-containers
sudo apt-get update
sudo apt-get install -y git python3-pip
git clone --depth=1 https://github.com/dusty-nv/jetson-containers
cd jetson-containers
pip3 install -r requirements.txt
Clone the MLC-LLM helper scripts:
cd ./data
git clone https://github.com/LJ-Hao/MLC-LLM-on-Jetson-Nano.git
cd ..
[OK] when both repos are cloned and requirements installed. [STOP] if git clone fails.
Phase 3 — Pull MLC Docker image and download Llama2 model
Replace <YOUR-ACCESS-TOKEN> with your HuggingFace token:
./run.sh --env HUGGINGFACE_TOKEN=<YOUR-ACCESS-TOKEN> $(./autotag mlc) \
/bin/bash -c 'ln -s $(huggingface-downloader meta-llama/Llama-2-7b-chat-hf) /data/models/mlc/dist/models/Llama-2-7b-chat-hf'
Verify the Docker image was created:
sudo docker images | grep mlc
[OK] when MLC image is listed and model download completed. [STOP] if image not found or download failed.
Phase 4 — Quantize the model with MLC
./run.sh $(./autotag mlc) \
python3 -m mlc_llm.build \
--model Llama-2-7b-chat-hf \
--quantization q4f16_ft \
--artifact-path /data/models/mlc/dist \
--max-seq-len 4096 \
--target cuda \
--use-cuda-graph \
--use-flash-attn-mqa
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 · 146 lines · 82 tokens per session scan B 384e710d53f5
quantized-llama2-7b-mlc is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 1,216 once invoked, about $0.0004 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.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.