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 openclaw-local-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/openclaw-local-deploy)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/openclaw-local-deploy"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/openclaw-local-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/openclaw-local-deploy"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/openclaw-local-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 | $0.00068 | $0.01271 |
| Opus 5.5 | $0.00027 | $0.00508 |
| Sonnet 5.5 | $0.00014 | $0.00254 |
| Haiku 4.5 | $0.00007 | $0.00127 |
Grade D, and why
openclaw-local-deploy 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.
| Ollama install script fails | Check internet. Verify `curl` is installed: `sudo apt install curl`. | 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local OpenClaw (Clawdbot) on reComputer Jetson with Ollama
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 Jetson series (e.g., reComputer Robotics J5011) |
| RAM | ≥ 16 GB |
| JetPack | 6.2 pre-installed |
| Storage | NVMe SSD recommended for faster model loading |
| Network | Stable internet for initial setup |
| Optional | Discord Bot Token (or WhatsApp — not mandatory) |
Phase 1 — Preflight
Verify JetPack version, available RAM, and network.
cat /etc/nv_tegra_release
free -h
ping -c 3 ollama.com
Expected: R36.x (JP6.2), ≥16 GB RAM, ping succeeds. [OK] when all pass. [STOP] if RAM < 16 GB or no network.
Phase 2 — Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
Verify the local API is reachable:
curl http://localhost:11434/api/tags
Expected: JSON response with no errors. [OK] when API responds. [STOP] if connection refused.
Phase 3 — Pull a local model
ollama pull qwen3-vl:2b
List installed models:
ollama list
Expected: qwen3-vl:2b appears in the list. [OK] when model is downloaded. [STOP] if download fails.
Phase 4 — Install OpenClaw
curl -fsSL https://openclaw.ai/install.sh | bash
The installer will enter a configuration wizard. You can skip the model configuration or temporarily set up a cloud model — the local model will be configured in the next phase.
[OK] when the setup wizard completes and OpenClaw is installed.
Phase 5 — Configure OpenClaw for local Ollama model
Edit the OpenClaw configuration file to add the Ollama provider:
nano ~/.openclaw/openclaw.json
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 · 172 lines · 68 tokens per session scan D 2351fd97cb6a
openclaw-local-deploy is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (56 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 1,271 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 2026-09-03.
Other skills, from other repositories
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 designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
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.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…