Guide · Agent skill
How to use the User Segmentation agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The User Segmentation skill is an agent skill that tells an AI agent how to group your users into at least three distinct segments based on what they do and what they are trying to get done, rather than only age or location. It is written for product managers, founders and researchers who have a pile of feedback and want one structured view of their customers. By the end of this guide you will know what the skill does, how to install it, how to trigger it with a natural request, and where its limits are.
User Segmentationphuryn/pm-skills · MIT Download the skill Quick start
- 1Download the ZIP. Download the skill ZIP from this page. It contains SKILL.md.
- 2Unzip into a skills folder. Unzip into ~/.claude/skills/ for personal use, or into .claude/skills/ inside a project.
- 3Or upload in the Claude apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the ZIP.
- 4Gather your feedback. Collect interviews, support tickets, surveys or usage data you want analyzed.
- 5Ask for a segmentation. Describe the task in plain words, such as segmenting users from your feedback, and attach or paste the data.
- 6Review the profiles. Check each segment against the real records and quotes before acting on it.
What does the User Segmentation skill make the agent do?
Once installed, the agent reads your user feedback, interviews, support tickets, surveys or usage data and looks for behavioral patterns, motivations and unmet needs across the user base. It then groups users into segments that serve different core needs.
For each segment, the skill asks the agent to write a profile covering typical behavior, jobs-to-be-done, pain points, how well your product fits that group, possible messaging, and whether to invest in, maintain or de-prioritize it. The skill is a structured analysis prompt. It does not run code or contact outside services by itself.
- Reads the feedback and data you provide directly
- Identifies at least three distinct segments
- Writes a profile for each segment, including quotes from your data where available
- Flags segments that may be underrepresented and suggests prioritization
How do you install the User Segmentation skill?
Download the ZIP for this skill from this page. The skill is distributed as a folder containing a SKILL.md file. You can install it in one of two ways.
Option one is a personal install for Claude Code: unzip the download into ~/.claude/skills/ so the skill is available in all your projects. Option two is a project install: unzip it into the .claude/skills/ folder inside a specific project, so only that project uses it.
Option three is for the Claude apps: upload the ZIP in the Claude apps under Settings → Capabilities → Skills.
- Personal (Claude Code): ~/.claude/skills/
- Project-level (Claude Code): .claude/skills/ inside your project
- Claude apps: Settings → Capabilities → Skills, then upload the ZIP
How do you trigger the skill with a natural request?
You do not need a special command. The skill describes itself as useful when segmenting a user base, analyzing diverse user feedback, or building a segmentation model, so phrase your request in those terms and attach or paste your data.
Name the data sources you have. The skill is designed to work with feedback, interviews, support tickets, product usage logs and surveys, so telling the agent which of these you are providing helps it organize the input.
- Segment our users based on these interview notes and support tickets.
- Analyze this feedback and find the distinct user groups we have.
- Help me build a user segmentation model from these survey responses and usage data.
- Which customer segments are hiding in this feedback export?
What output should you expect?
The skill asks for a consistent structure for every segment: a name and overview with estimated size, behavioral characteristics, jobs-to-be-done and motivations, key needs and pain points, current product fit, differentiated value proposition, and prioritization with a recommendation.
Because the output is text generated from your data, treat the segment sizes and profiles as hypotheses to check against your real records, not as measured facts.
- Segment name, overview and estimated size
- Behavioral characteristics and user journey
- Jobs-to-be-done and motivations
- Pain points and workarounds
- Current product fit and churn risk
- Value proposition and messaging
- Priority: invest, maintain or de-prioritize
What are the safety notes and limits?
According to the skill listing, it requires nothing beyond the agent itself, does not run scripts, and does not use the network. It consists of a single SKILL.md file.
The skill has clear limits. It is a qualitative reading of text, not a validated statistical model, so it is not a substitute for quantitative clustering on large datasets. With very thin feedback, it may invent segments the data cannot support. It focuses on behavior and needs, so it is not suited to decisions that depend on demographic or legal compliance data.
- Requires nothing listed; runs no scripts; uses no network
- Not a statistical clustering tool for large datasets
- Check output against real data, especially with small feedback sets
- Not designed for demographic or legal compliance decisions
Tips for better segments
The quality of the segments depends on the quality of the input. Give the agent a mix of sources, since the skill is built for diverse feedback. Including direct quotes and usage behavior makes the profiles more concrete than summaries alone.
The skill itself recommends grounding segments in behavior and motivation, using representative quotes, checking that segments do not overlap, and validating emerging segments against product usage data when you have it.
- Mix sources: interviews, tickets, surveys and usage data together
- Include verbatim quotes so profiles stay grounded
- Validate new segments against product usage or customer data
- Ask the agent to flag segments that feedback may underrepresent
- Review the segments with your team before making strategy decisions
What people use it for
- Finding hidden customer groups in a backlog of support tickets
- Preparing a segmentation model before a strategy meeting
- Combining interview notes and survey responses into one view of users
- Deciding which user groups to invest in, maintain or de-prioritize
Frequently asked questions
Is the User Segmentation skill free?
The source material for this listing does not state pricing or licence terms beyond the MIT licence shown on the item. The skill is listed under MIT, so check the licence file in the repository for the exact terms.
What do I need to use the User Segmentation skill?
The listing says it requires nothing beyond the agent itself. It runs no scripts and uses no network. You need an agent environment that supports skills, such as Claude Code or the Claude apps, and your own feedback data.
Where do I put the skill folder for Claude Code?
Unzip the download into ~/.claude/skills/ for a personal install, or into .claude/skills/ inside a project for a project-only install.
Can I install it in the Claude apps?
Yes. Upload the ZIP in the Claude apps under Settings → Capabilities → Skills.
Does the skill run code or send my data anywhere?
According to the listing, it does not run scripts and does not use the network. It is a structured analysis prompt in a SKILL.md file.
Is this a replacement for statistical clustering?
No. It is a qualitative reading of text, not a validated statistical model. For large datasets needing quantitative clustering, use dedicated analysis tools, and check any output from this skill against real data.
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Our verdict
Use this skill if you are a product manager, founder or researcher who wants a structured, behavior-based first pass at segmenting a mixed set of feedback before a strategy discussion. Pick something else if you need statistical clustering on large datasets, demographic or legal compliance analysis, or if your feedback set is very small, since the segments may not be supported by the data.
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