Attitudinal Segmentation

Community

Productfculty-aipm

attitudinal-segmentation

Use this skill when the user asks about "attitudinal segmentation", "segmenting by attitude", "AI embracer vs skeptic", "how to segment our users beyond demographics", "psychographic segmentation", "behavioral segmentation", "how users feel about AI", or wants to go beyond demographic user segments to understand attitudinal and behavioral differences that affect product and marketing decisions.

Overview

Publisher	Productfculty-aipm
Repository	`PM-Copilot-by-Product-Faculty`
Skill name	`attitudinal-segmentation`
Stars	80
Forks	34
Bundled files	Instructions only
Links	Skill source Repository Homepage

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 Productfculty-aipm on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/attitudinal-segmentation
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/attitudinal-segmentation

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/Productfculty-aipm/PM-Copilot-by-Product-Faculty.git /tmp/PM-Copilot-by-Product-Faculty
mkdir -p .claude/skills
cp -r /tmp/PM-Copilot-by-Product-Faculty/skills/attitudinal-segmentation .claude/skills/attitudinal-segmentation

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Attitudinal Segmentation 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 Attitudinal Segmentation 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 Attitudinal Segmentation 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.

Attitudinal Segmentation

You are helping the user move beyond demographic segmentation (role, company size, industry) to attitudinal segmentation — grouping users by how they think and behave, which predicts product adoption and communication effectiveness far better than demographics.

Framework: Hilary Gridley (AI embracer vs. skeptic segmentation), Lenny Rachitsky (counterintuitive advice for AI products).

Key principle: "The most meaningful segmentation for AI products is attitudinal: AI embracers vs. AI skeptics. Skeptics can become superusers — but they require a completely different journey." — Hilary Gridley, Lenny's Newsletter (2024)

Step 1 — Load Context

Read memory/user-profile.md and context/product/personas.md. Understand the existing segments and whether they're demographically or attitudinally defined.

Step 2 — The Core Attitudinal Dimensions

For any product, but especially AI-powered products, segment users on these dimensions:

Adoption attitude: How quickly and enthusiastically does the user adopt new tools?

Pioneer: Seeks out new tools actively; has a high tolerance for rough edges
Early adopter: Picks up tools when there's a clear value proposition; needs a bit less friction
Pragmatist: Adopts only when peers adopt; needs social proof and clear ROI
Conservative: Adopts last; needs extensive proof and low switching cost
Skeptic: Actively resists; may become a user only under external pressure

AI-specific attitude (especially relevant for PM Copilot):

AI Embracer: Excited; self-directed; builds habits quickly; tolerates setup friction
- Onboarding: jump to advanced features; show full capability surface
- Messaging: potential, speed, customization
AI Neutral: Pragmatic; will use AI if it saves time; skeptical of hype
- Onboarding: show concrete time saved before/after; quick wins first
- Messaging: efficiency, reliability
AI Skeptic: Trusts proven frameworks; fears losing craft or voice; needs to see before committing
- Onboarding: lead with framework credibility; show sample outputs first; emphasize control
- Messaging: augmentation not replacement; familiar frameworks; quality over speed

Craft orientation (relevant for knowledge work tools):

Craft-protective: Sees the product as a tool they control; quality of output matters as much as speed
Output-focused: Wants to move fast; good enough is fine; speed over polish
Collaborative: Uses the tool to think alongside; wants an interlocutor, not a generator

Step 3 — Apply to Your User Base

Based on research, analytics, or intuition, estimate the distribution of each segment in the user base:

For AI-attitude segmentation:

What % of your users are AI Embracers? (Signs: high feature adoption, exploring advanced commands, light touch on instructions)
What % are AI Skeptics? (Signs: low feature adoption, high support contact about AI outputs, explicit feedback about feeling like AI is "taking over")
What % are Neutral? (Signs: transactional use, specific task-oriented, don't explore)

If no data exists, structure a 3–5 question survey to measure attitudinal segments.

Step 4 — Implications by Segment

For each identified segment:

Product implications:

What features matter most to them?
What features would alienate them?
What onboarding experience do they need?

Retention implications:

What causes them to stay (or leave)?
What habit formation mechanism works for them?
What proactive check-in approach resonates?

Messaging implications:

What headline resonates?
What evidence format is most persuasive (testimonials, data, framework credibility)?
What words or phrases to avoid?

Step 5 — Conversion Paths

Identify the conversion path from Skeptic → Neutral → Embracer:

What specific experience or moment converts a Skeptic to a Neutral user? (Usually: a single output that impresses them and matches their quality bar)

What converts a Neutral to an Embracer? (Usually: discovering a second or third use case that makes the tool feel indispensable)

Design the product experience to facilitate these conversions deliberately.

Step 6 — Output

Produce:

Attitudinal segment definitions and estimated distribution for your product
Implications matrix (segment × product / retention / messaging)
Conversion path design
Top 3 actions to improve experience for the segment most at risk of churn

Offer to update context/product/personas.md with attitudinal layer and save insights to memory/user-profile.md.

Frequently asked questions

What does the Attitudinal Segmentation AI skill do?

Why use Attitudinal Segmentation on TypingMind?

Because you install it once and use it with any model. Attitudinal Segmentation 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 Attitudinal Segmentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/attitudinal-segmentation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Attitudinal Segmentation?

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 Attitudinal Segmentation?

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

Is the Attitudinal Segmentation AI skill free?

It is published on GitHub by Productfculty-aipm. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Attitudinal Segmentation

Step 1 — Load Context

Step 2 — The Core Attitudinal Dimensions

Step 3 — Apply to Your User Base

Step 4 — Implications by Segment

Step 5 — Conversion Paths

Step 6 — Output

Frequently asked questions

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