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Virtualization Operations

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cbrock84
virtualization-operations

Runs the hypervisor layer beneath the servers — host capacity and consolidation ratios, VM sprawl, snapshot discipline, resilience and live migration, and licensing that counts cores rather than instances. Use this to size or expand a cluster, work out why VMs are slow when the hosts look idle, clean up sprawl, set snapshot and template policy, or decide what stays virtualized and what moves to cloud.

Overview

Publishercbrock84
Repositoryheadcount
Skill namevirtualization-operations
Stars
2K
Forks
316
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cbrock84 on GitHub. Read the source before you install it.

Installation

Install the Virtualization Operations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cbrock84/headcount/tree/main/plugins/it-operations/skills/virtualization-operations
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cbrock84/headcount.git /tmp/headcount
mkdir -p .claude/skills
cp -r /tmp/headcount/plugins/it-operations/skills/virtualization-operations .claude/skills/virtualization-operations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Virtualization Operations in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Virtualization Operations on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Virtualization Operations is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Virtualization operations

Virtualization is the layer most infrastructure problems actually live in, and the one most monitoring is blind to. A guest reports healthy CPU while contending for a core it never gets. The usual failure is diagnosing upward — into the application — when the constraint is a host two layers below.

Consolidation ratio is a decision, not a result

Every cluster has a ratio of virtual resources to physical ones, and most organizations discover theirs rather than choosing it. Overcommitting memory and CPU is normal and correct; the question is how far, and what happens at peak rather than average.

Two numbers worth knowing at all times: what the cluster runs at now, and what it runs at when one host is gone. If losing a single host takes you past capacity, you have a cluster that survives hardware failure on paper and not in practice. Size for N-1 and keep the spare capacity visible in the budget, or the first thing cut is the headroom that makes the design work.

Memory is usually the real ceiling, not CPU. Cores oversubscribe gracefully; RAM does not.

Sprawl is a lifecycle problem wearing an infrastructure costume

VMs are trivially easy to create and socially difficult to delete. Nobody remembers what the box does, so it stays — consuming license, backup capacity, patching effort, and an attack surface nobody reviews.

The fix is ownership recorded at creation, not an annual cleanup. Every VM gets a named owner and a review date before it is built. Anything that cannot name an owner is a candidate for decommissioning, and the decommission path is: power off, wait a defined period, then delete. Powering off first turns an irreversible decision into a reversible one and produces the phone call that tells you what the machine did.

Templates and golden images keep the estate consistent. Builds that started from a template drift less than builds someone assembled, and drift is what makes patching unpredictable — see it-operations:systems-administration for the configuration baseline this feeds.

Snapshots are not backups, and old ones cause outages

A snapshot is a change log against a disk, and it grows until it is merged. Left running, it fills the datastore and takes down every VM sharing that storage, which is a much worse incident than whatever the snapshot was protecting against.

Treat them as a short-lived tool for a specific change: take one before a risky upgrade, merge it the same day, and alert on any snapshot older than the threshold you set. They are not a retention mechanism and they do not survive storage failure — real recovery belongs to it-operations:backup-and-recovery.

Live migration is a maintenance capability, not a resilience one

Moving running workloads between hosts is what makes patching the hypervisor possible without a maintenance window, and that alone justifies it. It does not protect against a host failing suddenly — that is a restart on another host, with downtime measured in boot time.

Know which of your workloads tolerate that restart and which do not. The ones that do not need clustering inside the guest, and virtualizing them does not remove that requirement.

Licensing counts what you provision, not what you use

Hypervisor and guest licensing is commonly priced per physical core or socket, which means a consolidation decision is also a licensing decision, and adding hosts can cost more in license than in hardware. Some vendors license every core in a cluster a workload could migrate to, not the one it runs on — which turns an availability setting into a bill.

Model the license cost before the hardware purchase, and check it again when the cluster grows. it-operations:it-asset-management owns the entitlement records; this skill owns knowing which architectural choices move them.

Where the boundary sits with cloud

Not everything should be virtualized on-premises, and not everything should move. The honest test is not cost per VM — it is whether the workload's demand is steady or spiky. Steady, predictable load runs cheaper on hardware you own. Spiky or seasonal load is what cloud elasticity is actually for.

Hybrid is the normal end state, not a transitional embarrassment. What makes it painful is running two operating models with two sets of habits, so decide deliberately which one owns identity, monitoring, and backup rather than letting each side answer differently.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Never

  • Provision a guest without an owner, a lifetime, and a decommission trigger.
  • Size a cluster with no headroom to lose a node during maintenance.
  • Grow a guest's resources before checking whether the guest itself is misbehaving. Oversizing hides the bug and eats the consolidation ratio.
  • Move a workload to cloud to escape a capacity problem you have not diagnosed on-premises.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Virtualization Operations AI skill do?

Runs the hypervisor layer beneath the servers — host capacity and consolidation ratios, VM sprawl, snapshot discipline, resilience and live migration, and licensing that counts cores rather than instances. Use this to size or expand a cluster, work out why VMs are slow when the hosts look idle, clean up sprawl, set snapshot and template policy, or decide what stays virtualized and what moves to cloud.

Why use Virtualization Operations on TypingMind?

Because you install it once and use it with any model. Virtualization Operations is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Virtualization Operations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/it-operations/skills/virtualization-operations. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Virtualization Operations?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Virtualization Operations?

As many as you like. As long as a model supports skills, you can use Virtualization Operations with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Virtualization Operations AI skill free?

Yes. It is published on GitHub by cbrock84 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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