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 llm-interface-controlgit 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/llm-interface-control)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/llm-interface-control"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/llm-interface-control/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/llm-interface-control"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/llm-interface-control.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00067 | $0.01161 |
| Opus 5.5 | $0.00027 | $0.00464 |
| Sonnet 5.5 | $0.00013 | $0.00232 |
| Haiku 4.5 | $0.00007 | $0.00116 |
Grade D, and why
llm-interface-control 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 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.
| GPIO/hardware errors | Mock hardware during development. Ensure Jetson GPIO permissions: `sudo usermod -aG gpio $USER`. | 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.
curl -fsSL https://ollama.com/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://ollama.com/install.sh | sh 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.
Local LLM Agent for Hardware Interface Control on Jetson
Use a local LLM running on Jetson to translate natural language commands into structured JSON for controlling hardware interfaces (lights, fans, thermostats, speakers) via GPIO, PWM, and I2C. The system uses Ollama with a custom prompt, FastAPI for the API layer, and confidence-based safety gating.
Execution model
Run one phase at a time. After each phase:
- Relay all output to the user.
- If output contains
[STOP]→ stop, consult the failure decision tree. - If output ends with
[OK]→ tell the user "Phase N complete" and proceed.
Prerequisites
| Requirement | Detail |
|---|---|
| Hardware | NVIDIA Jetson (Orin NX recommended) with JetPack |
| Python | 3.8+ |
| LLM Server | Ollama (installed in Phase 1) |
| Network | Local access for API calls |
Phase 1 — Install Ollama (~2 min)
curl -fsSL https://ollama.com/install.sh | sh
Verify:
ollama --version
[OK] when Ollama version is printed.
[STOP] if install fails — check network connectivity.
Phase 2 — Clone the project and install dependencies (~2 min)
git clone https://github.com/kouroshkarimi/llm_interface_controll.git
cd llm_interface_controll
pip install -r requirements.txt
[OK] when all packages install (FastAPI, uvicorn, etc.).
[STOP] if pip fails — check Python version.
Phase 3 — Create the custom Ollama model (~2 min)
The project includes a system prompt file that constrains the LLM to output only structured JSON with fields: intent, device, action, location, parameters, confidence.
cd llm_interface_controll
ollama create jetson-controller -f models/jetson-controller.txt
Verify:
ollama list | grep jetson-controller
[OK] when jetson-controller appears in the model list.
[STOP] if creation fails — ensure the base model (llama3.2:1b) is available: ollama pull llama3.2:1b.
Phase 4 — Start the FastAPI server (~1 min)
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 · 67 tokens per session scan D 6c396cda59d8
llm-interface-control is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (56 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 1,161 once invoked, about $0.0003 per session on Opus 5.5. 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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