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Service Desk

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cbrock84
service-desk

Runs the IT service desk — intake, triage, prioritization, escalation, knowledge, and the metrics that improve service rather than distort it. Use this to set up or fix a service desk, design ticket priority and escalation, reduce repeat contacts, structure a knowledge base, or work out why a desk hitting its targets still frustrates everyone.

Overview

Publishercbrock84
Repositoryheadcount
Skill nameservice-desk
Stars
2K
Forks
316
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Service Desk 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/service-desk
  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/service-desk .claude/skills/service-desk
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Service Desk 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 Service Desk 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 Service Desk 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.

Service desk

The service desk is where the whole IT organization is judged, usually by people having a bad day. Most of what makes it good is intake discipline and honest measurement.

Intake determines everything downstream

Capture enough at first contact to route correctly and act without a second exchange: who, what they were doing, what happened, what they expected, and how blocked they are.

Give the requester a single channel that works. Multiple half-supported channels — a queue, a chat, a shoulder tap, a manager's direct message — mean the loudest request wins rather than the most urgent, and the desk's workload becomes unmeasurable because most of it is invisible.

Priority is impact against urgency

Priority is not a feeling. Define it on two axes — how many people are affected and how blocked they are — and publish the matrix so it can be applied consistently rather than argued each time.

Keep incidents (something broken) separate from requests (something wanted). They have different clocks, different queues, and different success conditions, and merging them lets routine requests bury outages.

Escalation should be time-based and automatic. Relying on someone to notice a ticket aging means the tickets that age are the ones nobody is watching.

Measure service, not activity

Tickets closed measures activity, and optimizing it produces premature closure and reopened tickets. Better:

  • First-contact resolution — resolved without a handoff.
  • Time to resolution at the percentile users feel, not the mean.
  • Reopen rate — the direct check on premature closure.
  • Repeat contacts for the same underlying cause — the number that points at problems worth eliminating.

Never target an individual on volume. It reliably produces cherry-picking of easy tickets and quiet avoidance of hard ones.

Eliminate demand rather than absorbing it

A desk that handles the same failure two hundred times has done two hundred units of work and solved nothing. Cluster tickets by underlying cause and feed the top few into permanent fixes — a configuration change, a fix at source, or self-service that genuinely resolves.

Knowledge articles should be written for the person with the problem, not the person who fixed it: the symptom as experienced, then the steps. An article filed under the internal cause is not findable by anyone who does not already know the answer.

Tiers are a design decision, not a headcount ladder

Most desks describe themselves in tiers without having decided what defines one. The useful definition is not seniority — it is what a tier is permitted to resolve without escalating. Tier 1 owns the known and the documented. Tier 2 owns diagnosis: problems with no article yet. Tier 3 owns the systems themselves, and is the same people who build them.

Two failure modes, and they look opposite while sharing a cause:

  • Tiers drawn too high. Tier 1 escalates anything unfamiliar, so it becomes a routing desk and the expensive people handle password resets. The tell is a high escalation rate on tickets that close with a documented fix.
  • Tiers drawn too low. Tier 1 is expected to resolve things it has no access or training for, so it improvises, and the improvisations become the estate's undocumented configuration. The tell is repeat contacts on the same asset and fixes nobody can explain later.

Draw the boundary at access, not difficulty. A tier that cannot be granted the permission to fix a class of problem should not be the tier that owns it, and granting the permission anyway to avoid an escalation is how the least-experienced people end up with the most rights — see it-operations:identity-lifecycle-administration.

Write the escalation criteria down as conditions rather than judgment: elapsed time, blast radius, or a named system. "Escalate when you are stuck" produces wildly different behavior between two people on the same shift, and the quieter one escalates later.

Each tier needs its own definition of done. Tier 1 closing a ticket by handing it upward has not resolved anything, and counting that as a resolution is how a desk reports success while the backlog grows one layer up.

Tooling

ITSM platforms: Jira Service Management, Freshservice, ServiceNow, Ivanti Neurons, SolarWinds Service Desk, and similar. ServiceNow is a program rather than a purchase — do not take it on without the people to run it.

Remote support: TeamViewer, ScreenConnect, Splashtop, and similar.

Whatever the platform, a ticket has to carry the asset, the person, and a resolution category, or the reporting built on it is fiction.

Never

  • Run parallel unofficial intake channels and treat the ticket queue as the workload.
  • Merge incidents and requests into one queue.
  • Target individuals on ticket volume.
  • Close a recurring issue repeatedly without escalating it as a problem to eliminate.

Frequently asked questions

What does the Service Desk AI skill do?

Runs the IT service desk — intake, triage, prioritization, escalation, knowledge, and the metrics that improve service rather than distort it. Use this to set up or fix a service desk, design ticket priority and escalation, reduce repeat contacts, structure a knowledge base, or work out why a desk hitting its targets still frustrates everyone.

Why use Service Desk on TypingMind?

Because you install it once and use it with any model. Service Desk 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 Service Desk in TypingMind?

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

Which AI models can use Service Desk?

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 Service Desk?

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

Is the Service Desk 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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