Segmentation Analysis

Community

nimrodfisher

segmentation-analysis

Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.

Overview

Publisher	nimrodfisher
Repository	`data-analytics-skills`
Skill name	`segmentation-analysis`
Stars	493
Forks	87
Bundled files	3
License	MIT
Links	Skill source Repository

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.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nimrodfisher on GitHub. Read the source before you install it.

Installation

Install the Segmentation Analysis 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/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/segmentation-analysis
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/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/segmentation-analysis

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/nimrodfisher/data-analytics-skills.git /tmp/data-analytics-skills
mkdir -p .claude/skills
cp -r /tmp/data-analytics-skills/03-data-analysis-investigation/segmentation-analysis .claude/skills/segmentation-analysis

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

Use it in TypingMind

Enable Segmentation Analysis 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 Segmentation Analysis 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 Segmentation Analysis 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.

Segmentation Analysis

When to use

The team needs to understand who the best customers are and what distinguishes them
Marketing wants distinct groups to target with different messages or offers
Product needs to prioritise features based on high-value user behaviour patterns
Churn is high and the team needs to identify at-risk users before they leave
An existing segmentation feels arbitrary and needs data validation or improvement

Process

Define the segmentation goal — clarify what decisions the segments will inform (product roadmap, marketing campaigns, retention programs). The goal determines which variables matter and how many segments are useful (typically 3–7). See references/segmentation_approaches.md.
Select and prepare variables — choose 3–7 attributes or behaviours that vary across users and relate to the business outcome. Handle missing values and scale continuous variables. Remove outliers only if they would distort cluster centroids.
Run the segmentation — for data-driven segmentation, use k-means clustering via scripts/segmentation_runner.py. For rule-based segmentation, apply the business logic rules and validate that segments are distinct and non-overlapping.
Profile each segment — compute the mean and median for each variable by segment, expressed as % above/below the overall average. Identify the 2–3 defining characteristics of each segment and assign a descriptive name.
Validate and interpret — confirm segments are meaningfully different (silhouette score > 0.3 for clustering) and make business sense. Sanity-check by asking whether you would actually treat each segment differently.
Map to strategy and report — assign each segment to a recommended strategy (Retain & Expand, Monetise, Activate, Win-Back, Sunset). Produce assets/segment_profile_template.md with the profiles and strategic priorities.

Inputs the skill needs

User-level data with attributes (demographics, plan type) and behavioural metrics (sessions, revenue, feature usage, recency)
Business goal the segmentation will serve
Any existing segmentation to validate or replace
Minimum of ~100 users per expected segment for clustering to be meaningful

Output

scripts/segmentation_runner.py — runs k-means clustering, produces elbow and silhouette plots, assigns segment labels
references/segmentation_approaches.md — when to use k-means vs. RFM vs. rule-based; interpretation guide
assets/segment_profile_template.md — filled segment profiles with size, key characteristics, recommended strategy, and tracking plan

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 Segmentation Analysis AI skill do?

Why use Segmentation Analysis on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/segmentation-analysis. 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 Segmentation Analysis?

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 Segmentation Analysis?

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

Is the Segmentation Analysis AI skill free?

Yes. It is published on GitHub by nimrodfisher 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.

View all

Crosspost

affaan-m

CommunityPopular

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user…

Social Media Content Calendar

NousResearch

OrganizationPopular

Plan multi-platform social campaigns: briefs to posting.

Design Taste Frontend

Leonxlnx

CommunityPopular

tasteskill v2, the anti-slop frontend skill. Art direction for websites, distilled from a study of 247 curated gallery sites (One Page Love, Awwwards Sites of the Day). Deals researched directions…

Magazine Web Ppt

For marketing and gtm work: bind launches, campaigns, events, and brand plans to growth and pipeline outcomes. Built around the core query "annual-marketing-plan", with GTM strategy lead judgment,…

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Segmentation Analysis

When to use

Process

Inputs the skill needs

Output

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now