j6-board-monitor

j6-board-monitor is a skill for Claude Code, Codex from HorizonRobotics/OE-Skills. It costs 216 tokens per session (7,666 once invoked), scanned C, original, Apache-2.0.

A tool for measuring a J6 development board’s processor load, DDR memory bandwidth, and memory use over SSH. It can collect these readings while computer-vision or language models run, or while the board is idle.

In plain words
What is it for?
It is for checking resource use during fixed-rate vision inference, repeated language-model inference, or standalone board monitoring. It also helps compare models and configurations for vehicle or other edge-device designs.
Why use it?
It shows how much board hardware a model uses under a chosen workload and frame rate. This helps reveal whether a model or configuration fits the target device before deployment.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit It is for checking resource use during fixed-rate vision inference, repeated language-model inference, or standalone board monitoring. It also helps compare models and configurations for vehicle or other edge-device designs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/horizonrobotics/oe-skills/j6-board-monitor
Install

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.

Any agent
npx skills add HorizonRobotics/OE-Skills --skill j6-board-monitor
Clone the repo
git clone --depth 1 https://github.com/HorizonRobotics/OE-Skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for j6-board-monitor

README.md
[![agentmods](https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/j6-board-monitor/github.svg)](https://agentmods.dev/skills/horizonrobotics/oe-skills/j6-board-monitor)
Your own site
<a href="https://agentmods.dev/skills/horizonrobotics/oe-skills/j6-board-monitor"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/j6-board-monitor/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.

agentmods 80×15 button for j6-board-monitor

Your own site · 80×15
<a href="https://agentmods.dev/skills/horizonrobotics/oe-skills/j6-board-monitor"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/j6-board-monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 216 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,666 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00216 $0.07666
Opus 5 $0.00108 $0.03833
Sonnet 5 $0.00043 $0.01533
Haiku 4.5 $0.00022 $0.00767

Measured 12d ago against content hash 65b9fc4c4681, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade C, and why

j6-board-monitor scanned grade C 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 12d 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

ssh root@<IP> "rm -rf ${BOARD_WORKDIR}/*.hbm ${BOARD_WORKDIR}/*.log ${BOARD_WORKDIR}/*.csv ${BOARD_WORKDIR}/*.txt ${BOARD_WORKDIR}/*.sh ${BOARD_WORKDIR}/hrt_model_exec*"
horizon/skills/ucp/j6-board-monitor/SKILL.md · 544 lines

How it starts

The opening of the file, as written. The whole thing — 544 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Board Monitor

通过 SSH 在 J6 开发板上执行 BPU 占用率、DDR 带宽、内存使用的实时监控。支持三种场景:

  • Scenario A:CV 模型在受控帧率推理期间同步采集资源数据(使用 hrt_model_exec
  • Scenario B:独立监控板端硬件资源(无推理负载)
  • Scenario C:LLM 模型在循环推理期间同步采集资源数据(使用 simple_demo_request

适用场景

  • 在指定帧率(如 10Hz)推理时监控 BPU 占用率、DDR 带宽、内存使用
  • 独立监控板端硬件资源(无推理负载)
  • LLM 模型板端推理期间的 BPU/DDR/内存监控(循环推理 + 同步采集)
  • 评估模型在实车设计帧率下的资源消耗
  • 对比不同模型/配置的板端资源占用

触发关键词:BPU 监控、DDR 带宽、内存使用、资源监控、设定帧率、10Hz 推理、LLM 推理监控、LLM 资源、simple_demo_request、hrt_ucp_monitor、hrut_ddr

部署前预检:部署模型到板端前,先阅读 board-preflight.md 检查 ION 内存容量、L2M 配置和模型-板端兼容性。

工作流程

严格按照以下 8 步顺序执行,不可跳步。

前置条件

开始工作流之前,确认以下条件满足:

  1. .horizon/.env.board 必须存在:读取该文件获取 BOARD_IPBOARD_TYPEBOARD_WORKDIR。如果文件不存在或不完整,先触发 board-detection skill 完成板卡检测,再继续。
  2. SSH 免密登录:本 skill 的所有 SSH/SCP 命令假设已配置免密登录(密钥认证)。如果 SSH 连接要求输入密码,提示用户先配置密钥认证或提供密码,然后在所有 ssh/scp 命令中通过 sshpass -p <password> 前缀传递。
  3. OE 工具链可用hrt_ucp_monitorhrut_ddrhrt_model_exec 须已部署到板端。Step 2 会验证这一点。

Step 1:收集信息

.env.board 和用户输入收集以下信息。优先从 .env.board 读取,仅在文件中缺少时才询问用户。

项目 必填 默认值/来源 说明
板端 IP .env.boardBOARD_IP 优先从文件读取
SSH 用户名 root
认证方式 密钥(免密) 见前置条件第 2 条
板端工作目录 .env.boardBOARD_WORKDIR 优先从文件读取
模型文件路径 Scenario A 必填 - 板端路径或本地路径(本地路径需 SCP 上传)
目标帧率 (FPS) Scenario A 必填 - 如 10Hz、30Hz
监控时长 (秒) 30 采集持续时间
监控目标 bpu,ddr,memory 选择监控项

场景自动检测

  • 用户提供了 CV 模型(.hbm 较小、有明确 FPS 需求) → Scenario A(受控推理 + 同步监控)
  • 用户仅提供监控需求 → Scenario B(独立监控,无推理负载)
  • 用户提供了 LLM/VLM 模型(.hbm 较大、无 FPS 控制需求) → Scenario C(循环推理 + 同步监控)

Scenario A vs C 判断依据

  • 模型文件 > 500MB 或用户明确提到 LLM/VLM → Scenario C
  • 模型文件 < 500MB 且有明确帧率要求(如 10Hz) → Scenario A

Step 2:检查板卡连通性与平台

  1. 读取 .horizon/.env.board,获取 BOARD_TYPEBOARD_IPBOARD_WORKDIR
  2. SSH 连通性检查:ssh -o ConnectTimeout=5 root@<IP> "echo ok"
  3. 平台检测(决定 hrut_ddr 参数):
# 读取平台类型
BOARD_TYPE=$(grep '^BOARD_TYPE=' .horizon/.env.board | head -1 | cut -d= -f2)

if echo "$BOARD_TYPE" | grep -q "nash-p"; then
  # J6P: 4 BPU cores, hrut_ddr 需要 per-core 参数
  DDR_TYPE="bpu_p0"
  BPU_CORES=4
else
  # J6E: 1 BPU core, hrut_ddr 使用统一参数
  DDR_TYPE="bpu"
  BPU_CORES=1
fi

Read the full file on GitHub · 544 lines

Files

What ships with it

3 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.

Changes

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.

  1. 12d ago First seen · 544 lines · 216 tokens per session scan C 65b9fc4c4681

Subscribe to this mod's changes

j6-board-monitor is a skill published in the GitHub repository HorizonRobotics/OE-Skills (19 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 216 tokens to every session and 7,666 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

gke-compute-classes

Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto…

google/skills · 83 tokens

jetson-diagnostic

Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.

NVIDIA/skills · 30 tokens

doca-socket-relay

Use this skill when the operator is driving the DOCA Socket Relay to bridge a socket-oriented host application onto a BlueField DPU peer without rewriting it — picking the deployment shape (in-process, sidecar, or BlueField service container), configuring the host-side socket and the DPU-side forwarding endpoint…

NVIDIA/skills · 236 tokens

offensive-z-wave

Z-Wave attack methodology — sniffing with Z-Force / EZ-Wave / RTL-SDR + ZniffMobile, S0 (legacy) network-key derivation flaw and key reuse, S2 (modern) ECDH commissioning analysis, replay/injection on unauthenticated nodes, default-key brute-force on test deployments, and home-automation hub pivots. Use when targeting…

SnailSploit/Claude-Red · 113 tokens

hsb-flash

Flash the FPGA on an HSB board connected to an NVIDIA devkit. Supports HSB Lattice boards (FPGA versions 2407, 2412, 2507, 2510) and Leopard Imaging VB1940 "all-in-one" cameras (FPGA versions 2507, 2510). Uses release-specific YAML manifests and board-type-specific program commands. Lattice and VB1940 commands must…

NVIDIA/skills · 94 tokens

jetson-validate-image

Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.

NVIDIA/skills · 50 tokens