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Customer Onboarding And Implementation

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cbrock84
customer-onboarding-and-implementation

Takes a new customer from signature to working — setting a definition of live that both sides agreed before the contract was signed, planning and staffing the implementation, running data migration and integration realistically, training the people who will actually use it, and handing over to the ongoing relationship. Use this to design an onboarding motion, rescue a stalled implementation, work out why customers who bought never went live, or scope the services a deal actually needs.

Overview

Publishercbrock84
Repositoryheadcount
Skill namecustomer-onboarding-and-implementation
Stars
1.6K
Forks
237
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 Customer Onboarding And Implementation 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.
  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/customer-experience/skills/customer-onboarding-and-implementation .claude/skills/customer-onboarding-and-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Onboarding And Implementation 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 Customer Onboarding And Implementation 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 Customer Onboarding And Implementation 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.

Customer onboarding and implementation

The period between signature and first real use is where the largest share of preventable churn is created, and where a customer's opinion of the product is formed permanently.

Agree what live means before the contract is signed

The most common implementation failure happens in the sales cycle: nobody wrote down what success would look like, so the project has no finish line and every party quietly holds a different one.

Define the go-live criteria concretely — which teams, doing what, with what data in place — along with the date, who does what on each side, and what the customer must supply. Put it in the contract or an agreed plan before the deal closes, not after.

Sales commitments become implementation scope. If a deal was won on something the product does not do yet, the implementation team inherits it. Route that decision back to whoever can make it rather than absorbing it silently.

Plan the implementation as a project with a customer-side owner

Nothing predicts implementation failure like the absence of a named, empowered owner on the customer side. They need authority to make decisions and time genuinely allocated. If the customer cannot name that person, the project is at risk before it starts, and saying so early is a service.

Set a cadence from the beginning and hold it — a stalled implementation almost never announces itself; the calls quietly stop being attended.

Sequence for early value, not for completeness

Get one team doing one useful thing quickly, then widen. A phased approach produces a customer who has seen the product work, which changes every subsequent conversation.

The alternative — configure everything, migrate everything, launch to everyone — pushes all the value past a long window during which nothing works and the sponsor is defending the purchase.

Data and integrations are where the time actually goes

Both are routinely under-scoped because both depend on the customer's environment rather than yours. Profile their data early; expect it to be worse than described. For the mechanics of moving it, technology:data-migration covers the discipline.

Integration work depends on the customer's other vendors, their change windows, and their security review — none of which you control. Establish those constraints in week one rather than discovering them in week six.

Train for the job, not for the product

Feature training teaches people what the buttons do. Task training teaches them how to do their actual work in the new system, and only the second changes behavior.

Train close to go-live, not months before, and train the people who will use it rather than only the project team. Leave behind material they can use without you.

Hand over deliberately

An implementation that ends when the project ends leaves the account with nobody. Hand over with context — what was configured and why, what was deferred, what is fragile, who the people are — and confirm the receiving side has it rather than assuming.

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

  • Start an implementation without written go-live criteria both sides agreed.
  • Proceed without a named, empowered owner on the customer side.
  • Discover the customer's integration and security constraints after committing to a date.
  • Close an implementation on configuration complete rather than on people using it.

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 Customer Onboarding And Implementation AI skill do?

Takes a new customer from signature to working — setting a definition of live that both sides agreed before the contract was signed, planning and staffing the implementation, running data migration and integration realistically, training the people who will actually use it, and handing over to the ongoing relationship. Use this to design an onboarding motion, rescue a stalled implementation, work out why customers who bought never went live, or scope the services a deal actually needs.

Why use Customer Onboarding And Implementation on TypingMind?

Because you install it once and use it with any model. Customer Onboarding And Implementation 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 Customer Onboarding And Implementation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/customer-experience/skills/customer-onboarding-and-implementation. 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 Customer Onboarding And Implementation?

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 Customer Onboarding And Implementation?

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

Is the Customer Onboarding And Implementation 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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