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Customer Research

CommunityPopular
cbrock84
customer-research

Plans, runs, and synthesizes customer research — interviews, surveys, win-loss analysis, and message testing — into findings that change decisions. Use this when the customer is being guessed at, before positioning or a major launch, when churn or conversion has an unexplained cause, or when a team is arguing from anecdotes. Also use to turn raw interview notes into usable findings.

Overview

Publishercbrock84
Repositoryheadcount
Skill namecustomer-research
Stars
2K
Forks
316
Bundled files
1
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 Research 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/marketing/skills/customer-research
  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/marketing/skills/customer-research .claude/skills/customer-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Customer Research 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 Research 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 Research 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 research

Research earns its cost only if a decision hangs on it. Name that decision before recruiting anyone.

Choose the method by question

  • Why did this happen / what do they actually do — interviews. Small n, deep.
  • How common is this — survey. Only after interviews have told you what to ask; a survey written from assumptions returns your assumptions.
  • Why did we win or lose — win-loss, with losses weighted higher. Losses are where the truth is and are systematically under-sampled.
  • Which framing works — message testing against a real choice, not a preference rating.

Interviewing

Ask about the past, not the future. "What did you do last time" is evidence; "would you use this" is politeness.

  • Open broad, follow the energy, and go quiet after their first answer — the second one is where the substance is.
  • Chase specifics: when, what happened next, what did that cost you, who else was involved.
  • Never describe your solution before you have their problem in their words. Once they know what you want to hear, the interview is over.
  • Watch for the workaround. Anything someone has built a spreadsheet to survive is a validated problem.

Synthesizing

Code the transcripts before forming a view — findings assembled to support an existing belief will be. For each finding record: the claim, how many independently said it, a verbatim quote, and what would change if it is true. A finding that changes nothing is trivia.

Separate what people said, what they did, and what you inferred. Conflating the three is the most common way research misleads.

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

  • Recruit only happy customers. The sample that answers your email is not your market.
  • Lead: "How much would this feature help?" has one answer.
  • Present a single interview as a finding.

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 Research AI skill do?

Plans, runs, and synthesizes customer research — interviews, surveys, win-loss analysis, and message testing — into findings that change decisions. Use this when the customer is being guessed at, before positioning or a major launch, when churn or conversion has an unexplained cause, or when a team is arguing from anecdotes. Also use to turn raw interview notes into usable findings.

Why use Customer Research on TypingMind?

Because you install it once and use it with any model. Customer Research 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 Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/marketing/skills/customer-research. 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 Research?

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 Research?

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

Is the Customer Research 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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