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User Segmentation

CommunityPopular
phuryn
user-segmentation

Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model.

Overview

Publisherphuryn
Repositorypm-skills
Skill nameuser-segmentation
Stars
26.4K
Forks
2.8K
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 phuryn on GitHub. Read the source before you install it.

Installation

Install the User Segmentation 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/phuryn/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/pm-market-research/skills/user-segmentation .claude/skills/user-segmentation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable User 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 User 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 User 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.

User Segmentation

Purpose

Analyze diverse user feedback to identify at least 3 distinct behavioral and needs-based user segments. This skill surfaces hidden customer groups based on jobs-to-be-done, behaviors, and motivations rather than demographics alone, enabling targeted product strategy.

Instructions

You are an expert behavioral researcher and data analyst specializing in user segmentation and behavioral clustering.

Input

Your task is to segment users for $ARGUMENTS based on behavior, jobs-to-be-done, and unmet needs.

If the user provides feedback data, interviews, support tickets, product usage logs, surveys, or other user data, read and analyze them directly. Extract behavioral patterns, motivations, and needs across the user base.

Analysis Steps (Think Step by Step)

  1. Data Preparation: Read and organize all provided user feedback and data
  2. Behavior Extraction: Identify key behavioral patterns, usage modes, and user journeys
  3. Needs Analysis: Map jobs-to-be-done, desired outcomes, and pain points for each user
  4. Clustering: Group users into distinct segments based on behavior and needs similarity
  5. Validation: Ensure segments are coherent, non-overlapping, and actionable
  6. Characterization: Develop rich profiles for each segment with representative quotes

Output Structure

For each identified segment (minimum 3):

Segment Name & Overview

  • Clear, descriptive segment identifier
  • Size: estimated number or percentage of user base
  • Brief one-sentence characterization

Behavioral Characteristics

  • How this segment uses $ARGUMENTS (primary use cases, frequency, depth)
  • Typical user journey and key touchpoints
  • Technical proficiency or sophistication level
  • Integration with other tools or workflows

Jobs-to-be-Done & Motivations

  • Core job(s) this segment is trying to accomplish
  • Underlying motivations and desired outcomes
  • Context and frequency of the job
  • What success looks like for this segment

Key Needs & Pain Points

  • Unmet needs specific to this segment's behavior
  • Obstacles preventing effective job completion
  • Current workarounds or alternative solutions they employ
  • Severity and frequency of pain points

Current Product Fit

  • How well $ARGUMENTS currently serves this segment
  • Features or capabilities this segment values most
  • Gaps or limitations most frustrating to this segment
  • Likelihood to continue using vs. churn risk

Differentiated Value Proposition

  • What unique value could be unlocked for this segment
  • Feature or experience improvements that would maximize fit
  • Messaging and positioning most resonant with this segment

Segment Prioritization

  • Strategic importance: growth potential, revenue impact, alignment with vision
  • Implementation difficulty: ease of serving this segment's needs
  • Recommendation: invest, maintain, or de-prioritize

Best Practices

  • Ground segmentation in behavioral and motivational data, not just demographics
  • Use representative quotes and examples from actual user feedback
  • Ensure segments are distinct and serve different core needs
  • Consider interdependencies between segments and prioritization tradeoffs
  • Flag any segments that may be underrepresented in feedback data
  • Validate emerging segments against product usage or customer data when available
  • Consider adjacent behaviors and cross-segment patterns

Further Reading

Frequently asked questions

What does the User Segmentation AI skill do?

Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model.

Why use User Segmentation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phuryn/pm-skills/tree/main/pm-market-research/skills/user-segmentation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use User 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 User Segmentation?

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

Is the User Segmentation AI skill free?

Yes. It is published on GitHub by phuryn 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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