OpenClaw

system-watchdog

System resource monitoring that detects wasteful or suspicious processes. Outputs structured JSON for any consumer.

brennerspear0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install brennerspear/system-watchdog

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

System Watchdog

Monitor system resources and flag wasteful or suspicious processes. Works standalone as a bash script — see openclaw.md for scheduled cron setup.

Standalone Usage

Run the check script directly — no OpenClaw required:

bash check.sh

Outputs a JSON object to stdout. Parse it however you like — pipe to jq, feed to an agent, integrate into your own monitoring stack.

Output Format

{
  "suspicious": true,
  "summary": {
    "ram": "12.3/31.2 GB (39%)",
    "swap": "0.5/8.0 GB (6%)",
    "load": "1.2/0.8/0.6",
    "cores": 8,
    "disk": "120/256 GB (45%)"
  },
  "issues": [
    {
      "type": "high_ram",
      "description": "claude (PID 1234) 4650MB RAM",
      "details": { "pid": 1234, "name": "claude", "cpu_pct": 2.1, "mem_mb": 4650, "elapsed": "3d" }
    }
  ],
  "top_processes": [
    { "pid": 1234, "name": "claude", "cpu_pct": 2.1, "mem_mb": 4650, "elapsed": "3d" }
  ]
}

suspicious: true → at least one issue exceeded a threshold
suspicious: false → system looks healthy

Thresholds

Check	Threshold	Issue Type
Process RAM	> 4096 MB	`high_ram`
Process CPU	> 50%	`high_cpu`
Stale processes	Running > 2 days AND using > 100 MB or > 1% CPU	`stale`
Disk usage	> 80% on root mount	`disk`

Common Offenders

claude / codex — AI coding agents left running for days
whisper / whisper-server — speech-to-text servers consuming GPU/RAM
python / python3 — runaway scripts or leaked processes
node — dev servers or builds that never stopped

Agent Workflow (for AI agents)

Run check.sh
Parse the JSON output
If suspicious is false → do nothing (no report needed)
If suspicious is true → format a concise report and notify the user

Suggested Report Format

⚠️ System Watchdog Report

📊 System: RAM 12.3/31.2 GB (39%) | Swap 0.5/8.0 GB (6%) | Load 1.2/0.8/0.6
💾 Disk: / 45% (120/256 GB)

🔴...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/brennerspear/system-watchdog
Version: —
Commit: 872cc87ca737bedbb337f822d433443f3ee1aa60
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# System Watchdog

Monitor system resources and flag wasteful or suspicious processes. Works standalone as a bash script — see `openclaw.md` for scheduled cron setup.

## Standalone Usage

Run the check script directly — no OpenClaw required:

```bash
bash check.sh
```

Outputs a JSON object to stdout. Parse it however you like — pipe to `jq`, feed to an agent, integrate into your own monitoring stack.

### Output Format

```json
{
  "suspicious": true,
  "summary": {
    "ram": "12.3/31.2 GB (39%)",
    "swap": "0.5/8.0 GB (6%)",
    "load": "1.2/0.8/0.6",
    "cores": 8,
    "disk": "120/256 GB (45%)"
  },
  "issues": [
    {
      "type": "high_ram",
      "description": "claude (PID 1234) 4650MB RAM",
      "details": { "pid": 1234, "name": "claude", "cpu_pct": 2.1, "mem_mb": 4650, "elapsed": "3d" }
    }
  ],
  "top_processes": [
    { "pid": 1234, "name": "claude", "cpu_pct": 2.1, "mem_mb": 4650, "elapsed": "3d" }
  ]
}
```

- `suspicious: true` → at least one issue exceeded a threshold
- `suspicious: false` → system looks healthy

### Thresholds

| Check | Threshold | Issue Type |
|-------|-----------|------------|
| Process RAM | > 4096 MB | `high_ram` |
| Process CPU | > 50% | `high_cpu` |
| Stale processes | Running > 2 days AND using > 100 MB or > 1% CPU | `stale` |
| Disk usage | > 80% on root mount | `disk` |

### Common Offenders

- `claude` / `codex` — AI coding agents left running for days
- `whisper` / `whisper-server` — speech-to-text servers consuming GPU/RAM
- `python` / `python3` — runaway scripts or leaked processes
- `node` — dev servers or builds that never stopped

## Agent Workflow (for AI agents)

1. Run `check.sh`
2. Parse the JSON output
3. If `suspicious` is `false` → do nothing (no report needed)
4. If `suspicious` is `true` → format a concise report and notify the user

### Suggested Report Format

```
⚠️ System Watchdog Report

📊 System: RAM 12.3/31.2 GB (39%) | Swap 0.5/8.0 GB (6%) | Load 1.2/0.8/0.6
💾 Disk: / 45% (120/256 GB)

🔴...

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