OpenClaw

openclaw-network-diagnostics

Standalone advanced network diagnostics for OpenClaw to continuously test end-to-end connectivity from OpenClaw agent to Telegram Bot API and approximate delivery to a personal Telegram client. Use when investigating latency spikes, packet loss, DPI/throttling/blocking suspicion, DNS instability, TLS/TCP issues, route changes, MTU shifts, retry/timeout behavior, or Telegram rate-limits with structured rotating JSON logs.

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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 h8kxrfp68z-lgtm/openclaw-network-diagnostics

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

OpenClaw Network Diagnostics

Overview

Run a pure network diagnostic worker from CLI to continuously monitor connectivity between:

OpenClaw runtime host
Telegram Bot API (api.telegram.org)
Personal Telegram client approximation via delivery verification cycles

Keep diagnostics isolated from OpenClaw LLM flow:

Use no LLM calls.
Consume no AI tokens.
Run in independent async worker loops.

Skill Files

scripts/netdiag.py: standalone CLI worker (run/start/stop/status/validate-config)
references/config.example.json: complete example configuration
references/example-log-entries.jsonl: sample structured JSON logs
references/openclaw-integration.md: integration patterns with pros/cons
references/ai-log-analysis.md: workflow for later AI-based log analysis

Prerequisites

Install and verify:

Python 3.11+
macOS networking tools: dig, ping, traceroute
Telegram bot token and personal chat id

Install

From skill root:

cd /Users/ivanbelugin/Documents/Connection\ Monitoring\ System/openclaw-network-diagnostics
python3 scripts/netdiag.py validate-config --config references/config.example.json

Create a real config file from the example and set real credentials:

cp references/config.example.json config.json

Then edit config.json:

telegram.bot_token
telegram.personal_chat_id

Run Model

Foreground mode (manual stop via Ctrl+C)

python3 scripts/netdiag.py run --config config.json --pid-file ./logs/netdiag.pid

Behavior:

Start manually from CLI.
Run continuously until manual stop.
Print JSON summary to stdout on stop.
Save summary to logging.summary_file_path.

Background mode (non-blocking service)

python3 scripts/netdiag.py start --config config.json --pid-file ./logs/netdiag.pid
python3 scripts/netdiag.py status --pid-file ./logs/netdiag.pid
python3 scripts/netdiag.py stop --pid-file ./logs/netdiag.pid

Use this mode to avoid bl...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/h8kxrfp68z-lgtm/openclaw-network-diagnostics
Version: —
Commit: cb3a2b88d14bc025ee2b3678643a7c7d128ca693
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

# OpenClaw Network Diagnostics

## Overview

Run a pure network diagnostic worker from CLI to continuously monitor connectivity between:
1. OpenClaw runtime host
2. Telegram Bot API (`api.telegram.org`)
3. Personal Telegram client approximation via delivery verification cycles

Keep diagnostics isolated from OpenClaw LLM flow:
- Use no LLM calls.
- Consume no AI tokens.
- Run in independent async worker loops.

## Skill Files

- `scripts/netdiag.py`: standalone CLI worker (`run/start/stop/status/validate-config`)
- `references/config.example.json`: complete example configuration
- `references/example-log-entries.jsonl`: sample structured JSON logs
- `references/openclaw-integration.md`: integration patterns with pros/cons
- `references/ai-log-analysis.md`: workflow for later AI-based log analysis

## Prerequisites

Install and verify:
1. Python `3.11+`
2. macOS networking tools: `dig`, `ping`, `traceroute`
3. Telegram bot token and personal chat id

## Install

From skill root:

```bash
cd /Users/ivanbelugin/Documents/Connection\ Monitoring\ System/openclaw-network-diagnostics
python3 scripts/netdiag.py validate-config --config references/config.example.json
```

Create a real config file from the example and set real credentials:

```bash
cp references/config.example.json config.json
```

Then edit `config.json`:
- `telegram.bot_token`
- `telegram.personal_chat_id`

## Run Model

### Foreground mode (manual stop via Ctrl+C)

```bash
python3 scripts/netdiag.py run --config config.json --pid-file ./logs/netdiag.pid
```

Behavior:
- Start manually from CLI.
- Run continuously until manual stop.
- Print JSON summary to stdout on stop.
- Save summary to `logging.summary_file_path`.

### Background mode (non-blocking service)

```bash
python3 scripts/netdiag.py start --config config.json --pid-file ./logs/netdiag.pid
python3 scripts/netdiag.py status --pid-file ./logs/netdiag.pid
python3 scripts/netdiag.py stop --pid-file ./logs/netdiag.pid
```