Vmware Aiops

VMware AIops

English | [中文](README-CN.md)

AI-powered VMware vCenter/ESXi monitoring and operations tool.

**Need read-only monitoring only?** See [VMware-Monitor](https://github.com/zw008/VMware-Monitor) — an independent repository with code-level safety (zero destructive code in the codebase).

[](https://clawhub.ai/skills/vmware-aiops) [](https://skills.sh/zw008/VMware-AIops) [](https://github.com/zw008/VMware-AIops) [](LICENSE)

Quick Install (Recommended)

Works with Claude Code, Cursor, Codex, Gemini CLI, Trae, and 30+ AI agents:

# Via Skills.sh
npx skills add zw008/VMware-AIops

# Via ClawHub
clawhub install vmware-aiops

PyPI Install (No GitHub Access Required)

# Install via uv (recommended)
uv tool install vmware-aiops

# Or via pip
pip install vmware-aiops

# China mainland mirror (faster)
pip install vmware-aiops -i https://pypi.tuna.tsinghua.edu.cn/simple

Claude Code Plugin Install

# Add marketplace
/plugin marketplace add zw008/VMware-AIops

# Install plugin
/plugin install vmware-ops

# Use the skill
/vmware-ops:vmware-aiops

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Capabilities Overview

CLI vs MCP: Which Mode to Use

Scenario	Recommended	Why
Local/small models (Ollama, Qwen <32B)	CLI	~2K tokens context vs ~10K for MCP; small models struggle with 31 tool schemas
Token-sensitive workflows	CLI	SKILL.md + Bash tool = minimal overhead
Cloud models (Claude, GPT-4o)	Either	Both work; MCP gives structured JSON I/O
Automated pipelines / Agent chaining	MCP	Type-safe parameters, structured output, no shell parsing

**Rule of thumb**: Use CLI for cost efficiency and small models. Use MCP for structured automation with large models.

Architecture

User (Natural Language)
  ↓
AI CLI Tool (Claude Code / Gemini / Codex / Aider / Continue / Trae / Kimi)
  ↓ reads SKILL.md / AGENTS.md / rules
  ↓
vmware-aiops CLI

...