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 agentmods add skills/seeed-projects/seeed-jetson-developtool/disk-encryptionnpx skills add Seeed-Projects/Seeed-Jetson-DevelopTool --skill disk-encryptiongit clone --depth 1 https://github.com/Seeed-Projects/Seeed-Jetson-DevelopToolWhat 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 | $0.00083 | $0.01940 |
| Opus 5 | $0.00042 | $0.00970 |
| Sonnet 5 | $0.00017 | $0.00388 |
| Haiku 4.5 | $0.00008 | $0.00194 |
Grade B, and why
disk-encryption 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 2d 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 tar xpf Tegra_Linux_Sample-Root-Filesystem_r36.4.3_aarch64.tbz2 -C Linux_for_Tegra/rootfs/ How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Disk Encryption for Jetson
Encrypts the Jetson root filesystem during the flashing process using OPTEE-generated keys. This is a destructive operation — the device must be re-flashed.
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.
WARNING: This process re-flashes the entire device. All existing data on the Jetson will be erased.
Prerequisites
| Requirement | Detail |
|---|---|
| Host PC | Ubuntu 20.04 or 22.04 (x86_64) |
| Jetson device | e.g. reComputer J401 (Orin) in recovery mode |
| L4T version | 36.4.3 (adjust commands for other versions) |
| USB cable | Connecting host to Jetson recovery port |
| Storage | ~50GB free on host for BSP and build artifacts |
Phase 1 — Prepare BSP workspace on host PC (~15 min)
Download and extract NVIDIA L4T BSP and root filesystem:
tar xf Jetson_Linux_r36.4.3_aarch64.tbz2
sudo tar xpf Tegra_Linux_Sample-Root-Filesystem_r36.4.3_aarch64.tbz2 -C Linux_for_Tegra/rootfs/
Sync kernel source:
cd Linux_for_Tegra/source/
./source_sync.sh -t jetson_36.4.3
cd ../..
Clone Seeed BSP overlay and apply:
sudo apt update && sudo apt install -y git-lfs
mkdir -p github/Linux_for_Tegra
git clone https://github.com/Seeed-Studio/Linux_for_Tegra.git -b r36.4.3 --depth=1 github/Linux_for_Tegra
cp -r github/Linux_for_Tegra/* Linux_for_Tegra/
cd Linux_for_Tegra
sudo ./apply_binaries.sh
[OK] when apply_binaries.sh completes. [STOP] if download or extraction fails.
Phase 2 — Install build dependencies and compile kernel (~20–30 min)
sudo apt-get install -y build-essential flex bison libssl-dev sshpass \
abootimg nfs-kernel-server libxml2-utils qemu-user-static
Set up cross-compiler (download the toolchain first):
mkdir -p l4t-gcc
tar xf aarch64--glibc--stable-2022.08-1.tar.bz2 -C ./l4t-gcc
export ARCH=arm64
export CROSS_COMPILE=$(pwd)/l4t-gcc/aarch64--glibc--stable-2022.08-1/bin/aarch64-buildroot-linux-gnu-
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.
- 2d ago First seen · 224 lines · 83 tokens per session scan B 5deb1fe40860
disk-encryption is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed 3d ago), licensed MIT. It adds 83 tokens to every session and 1,940 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…