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 deepseek-quick-deploygit 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/deepseek-quick-deploy)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/deepseek-quick-deploy"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/deepseek-quick-deploy/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/deepseek-quick-deploy"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/deepseek-quick-deploy.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 | $0.00043 | $0.00721 |
| Opus 5.5 | $0.00017 | $0.00288 |
| Sonnet 5.5 | $0.00009 | $0.00144 |
| Haiku 4.5 | $0.00004 | $0.00072 |
Grade C, and why
deepseek-quick-deploy scanned grade C 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 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.
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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepSeek-R1 Quick Deploy on Jetson
Hardware required: Jetson with >8GB RAM, JetPack 5.1.1+. This is the fastest path to running DeepSeek-R1 locally. For optimized MLC-based deployment with better throughput, see the
deploy-deepseek-mlcskill instead.
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 | Detail |
|---|---|
| RAM | >8 GB (unified memory) |
| JetPack | 5.1.1 or later |
| Internet | Required for install script and model download |
| Disk space | ≥10 GB free for model weights |
Phase 1 — Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
Verify:
ollama --version
# Expected: ollama version x.x.x
[OK] when ollama --version prints a version string. [STOP] if the install script fails — see failure decision tree.
Phase 2 — Run DeepSeek-R1
ollama run deepseek-r1
The first run downloads the model weights (~4–8 GB depending on quantization). Subsequent runs start immediately from cache.
Expected: after download, an interactive prompt appears:
>>> Send a message (/? for help)
Test with a simple prompt to confirm the model responds. Type /bye to exit.
[OK] when the model responds to a prompt. [STOP] if the process is killed or hangs — see failure decision tree.
Failure decision tree
| Symptom | Action |
|---|---|
curl install script fails with network error |
Check internet connectivity. If behind a proxy, set https_proxy env var before running curl. Try downloading the script manually and inspecting it. |
curl install script fails with permission error |
Run with sudo or ensure /usr/local/bin is writable. |
| Model download stalls or fails mid-way | Retry ollama run deepseek-r1 — Ollama resumes partial downloads. Check available disk space: df -h. |
| Process killed during model pull (OOM) | Not enough RAM. Free memory by stopping other processes, or use a smaller quantization: ollama run deepseek-r1:1.5b. |
| Model loads but inference is very slow | Expected on smaller Jetson modules. For better performance use the deploy-deepseek-mlc skill which uses MLC-optimized kernels. |
ollama: command not found after install |
Add Ollama to PATH: export PATH=$PATH:/usr/local/bin, then reload shell or open a new terminal. |
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 · 85 lines · 43 tokens per session scan C fcb6826763b6
deepseek-quick-deploy is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (56 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 721 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 2 findings (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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