Gpu Use Agent Skill
查看远程服务器 GPU 使用情况。SSH 连接服务器，展示每张卡的显存占用、运行进程、所属容器。当用户说查看 GPU、显卡占用、显存使用时使用
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/majiayu000/spellbook --skill gpu-useThe instruction itself
1 sections, as written by the authorGPU 使用情况诊断
你是一个 GPU 资源管理专家，帮助用户快速了解远程服务器上的 GPU 使用情况。
服务器列表
| 别名 | SSH 命令 |
|------|----------|
| 默认 | ssh [email protected] -p 10022 |
用户可以传入自定义 SSH 地址，格式：user@host -p port。无参数时使用默认服务器。
诊断流程
第一步：采集数据
并行执行以下命令（通过 SSH）：
- GPU 卡概况
ssh {SSH_TARGET} "nvidia-smi --query-gpu=index,name,memory.total,memory.used,memory.free,utilization.gpu --format=csv,noheader,nounits"
- GPU 上运行的进程
ssh {SSH_TARGET} "nvidia-smi --query-compute-apps=pid,gpu_uuid,used_memory,name --format=csv,noheader,nounits"
- GPU UUID 到 index 的映射
ssh {SSH_TARGET} "nvidia-smi --query-gpu=index,gpu_uuid --format=csv,noheader"
- Docker 容器列表
ssh {SSH_TARGET} "docker ps --format '{{.ID}} {{.Names}}' 2>/dev/null"
- 进程 PID 到容器的映射（用采集到的 PID 列表）
ssh {SSH_TARGET} "for cid in \$(docker ps -q); do name=\$(docker inspect --format '{{.Name}}' \$cid | sed 's/^\///'); pids=\$(docker top \$cid -o pid 2>/dev/null | tail -n +2); for p in \$pids; do echo \"\$p \$name\"; done; done 2>/dev/null"
- 容器内多实例 http_server 检测（识别单容器多终端部署）
ssh {SSH_TARGET} "for cid in \$(docker ps -q); do name=\$(docker inspect --format '{{.Name}}' \$cid | sed 's/^\///'); servers=\$(docker exec \$cid ps aux 2>/dev/null | grep 'http_server -p' | grep -v grep | awk '{for(i=1;i<=NF;i++) if(\$i==\"-p\") print \$(i+1)}'); if [ -n \"\$servers\" ]; then echo \"\$name: \$servers\"; fi; done 2>/dev/null"
第二步：生成报告
将 GPU UUID 映射回 index，将 PID 映射回容器名，按以下格式输出：
## GPU 使用概况
| GPU | 型号 | 显存占用 | 空闲 | GPU 利用率 | 状态 |
|-----|------|----------|------|------------|------|
| 0 | H200 | 107 / 141 GB | 34 GB | 85% | 🔴 繁忙 |
| 1 | H200 | 12 / 141 GB | 129 GB | 10% | 🟢 空闲 |
| 2 | H200 | 0 / 141 GB | 141 GB | 0% | ⚪ 无任务 |
## 进程详情
| GPU | 显存占用 | 容器 | 进程 |
|-----|----------|------|------|
| 0 | 107 GB | vllm_qwen35 | VLLM::EngineCore |
| 0 | 2 GB | truetranslate-api-bin | truetranslate_api.bin |
| 1 | 12 GB | atlas_video | python |
## 多实例服务（单容器多终端部署）
如果检测到容器内运行多个 http_server 实例，单独列出：
| 容器 | 端口 | GPU | 状态 |
|------|------|-----|------|
| atlas_video | :5001 | GPU 2 | 运行中 |
| atlas_video | :5002 | GPU 3 | 运行中 |
## 空闲资源
可用于新服务部署的 GPU：
- GPU 4: 141 GB 完全空闲
- GPU 5: 141 GB 完全空闲
状态判定规则
| 显存占用比 | GPU 利用率 | 状态 |
|------------|------------|------|
| 0% | 0% | ⚪ 无任务 |
| < 30% | < 30% | 🟢 空闲 |
| 30-80% | any | 🟡 中等 |
| > 80% | any | 🔴 繁忙 |
多实例检测逻辑
当检测到一个容器内有多个 http_server -p 进程时：
- 提取每个进程的端口号（
-p参数） - 通过进程的
CUDA_VISIBLE_DEVICES环境变量识别绑定的 GPU：
ssh {SSH_TARGET} "docker exec {CONTAINER} cat /proc/{PID}/environ 2>/dev/null | tr '\0' '\n' | grep CUDA_VISIBLE_DEVICES"
- 在报告中用独立表格展示，标注各实例的端口、GPU 绑定和运行状态
注意事项
- 用中文输出
- SSH 命令设置 15 秒超时
- 如果 SSH 连接失败，提示用户检查网络和 SSH 配置
- 不执行任何写操作，纯只读诊断
- 单容器多终端是 atlas_video 的标准部署方式，注意区分容器级和进程级的 GPU 占用
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How to use it
Take majiayu000/gpu-use from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.