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Agent skill

Segmentation Analysis

by nimrodfisher in nimrodfisher/data-analytics-skills

Customer/user segmentation with actionable insights. An agent skill from nimrodfisher/data-analytics-skills.

MITAuto-check passedData & Analytics

Install Segmentation Analysis

skills CLI
$ npx skills add nimrodfisher/data-analytics-skills --skill segmentation-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nimrodfisher/data-analytics-skills segmentation-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/03-data-analysis-investigation/segmentation-analysis .claude/skills/segmentation-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
segmentation-analysis
GitHub stars
465
Token cost
~718 tokens
SKILL.md length
335 words
Files
4 (incl. scripts, references, assets)
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Customer/user segmentation with actionable insights. An agent skill from nimrodfisher/data-analytics-skills.

  • Works in 6 steps: Define the segmentation goal — clarify… → Select and prepare variables — choose… → Run the segmentation — for data-driven… → …
  • Identifying distinct customer groups
  • Runs Python scripts from its folder
  • Analyzing segment-specific behavior

What it does

Segmentation Analysis is an agent skill from nimrodfisher/data-analytics-skills. Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.

Its SKILL.md is about 720 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts, reference files and assets (for example `assets/segment_profile_template.md`, `references/segmentation_approaches.md` and `scripts/segmentation_runner.py`).

It sits in Data & Analytics, covering Data analysis. The repository describes itself as: A comprehensive list of Claude & Codex skills for a wide range of data analytics tasks. The licence is MIT.

When your agent uses it

  • Identifying distinct customer groups
  • Analyzing segment-specific behavior
  • Profiling high-value segments
  • Testing segmentation hypotheses

Example prompts

  • “/segmentation-analysis”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Define the segmentation goal — clarify what decisions the segments will inform (product roadmap, marketing campaigns, retention programs)…
  2. Select and prepare variables — choose 3–7 attributes or behaviours that vary across users and relate to the business outcome. Handle…
  3. Run the segmentation — for data-driven segmentation, use k-means clustering via scripts/segmentation_runner.py. For rule-based…
  4. Profile each segment — compute the mean and median for each variable by segment, expressed as % above/below the overall average. Identify…
  5. Validate and interpret — confirm segments are meaningfully different (silhouette score > 0.3 for clustering) and make business sense…
  6. Map to strategy and report — assign each segment to a recommended strategy (Retain & Expand, Monetise, Activate, Win-Back, Sunset)…

What it can do on your machine

Read from SKILL.md and the folder at commit 9449d36. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Segmentation Analysis loads about 718 tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 335 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~718
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from nimrodfisher/data-analytics-skills at commit 9449d36, republished under its MIT licence (© nimrodfisher). 335 words, ~718 tokens.

Download SKILL.mdSave it as .claude/skills/segmentation-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
segmentation-analysis
description
Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.

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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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

© nimrodfisher, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references, assets) in 03-data-analysis-investigation/segmentation-analysis of nimrodfisher/data-analytics-skills.

  • SKILL.md
  • assets/segment_profile_template.md
  • references/segmentation_approaches.md
  • scripts/segmentation_runner.py

Open the folder on GitHubat commit 9449d36

Compare with similar skills

Segmentation Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Segmentation Analysis compared with similar skills
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Segmentation Analysis this skillnimrodfisher/data-analytics-skills465—~718Automated safety check: PassMIT
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Excel and CSV Data Analysisbytedance/deer-flow83k4 repos~2.2kAutomated safety check: PassMIT
Exploratory Data AnalysisOleafly/Oleafly2063 repos~3.4kAutomated safety check: NotesMIT
Python Executorcortega26/chile-hub1132 repos~1.5kAutomated safety check: PassMIT
Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT

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Categories

Questions about Segmentation Analysis

What does Segmentation Analysis do?

Customer/user segmentation with actionable insights. An agent skill from nimrodfisher/data-analytics-skills. Segmentation Analysis is an agent skill from nimrodfisher/data-analytics-skills. Customer/user segmentation with actionable insights.

When should I use Segmentation Analysis?

Segmentation Analysis fits situations like: identifying distinct customer groups; analyzing segment-specific behavior; profiling high-value segments; testing segmentation hypotheses.

How do I install Segmentation Analysis in Claude Code?

Run `npx skills add nimrodfisher/data-analytics-skills --skill segmentation-analysis -a claude-code`. Or copy the skill folder (03-data-analysis-investigation/segmentation-analysis in nimrodfisher/data-analytics-skills) into .claude/skills/segmentation-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Segmentation Analysis in Codex?

Run `npx skills add nimrodfisher/data-analytics-skills --skill segmentation-analysis -a codex`. Or copy the skill folder (03-data-analysis-investigation/segmentation-analysis in nimrodfisher/data-analytics-skills) into .agents/skills/segmentation-analysis in your project. Codex loads it when a task matches its description.

Can I use Segmentation Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nimrodfisher/data-analytics-skills --skill segmentation-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/segmentation-analysis, .gemini/skills/segmentation-analysis, .github/skills/segmentation-analysis and .opencode/skills/segmentation-analysis in your project.

What does Segmentation Analysis need to run?

Going by SKILL.md and its folder, Segmentation Analysis needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Segmentation Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Segmentation Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Segmentation Analysis use?

Segmentation Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Segmentation Analysis use?

About 718 tokens (SKILL.md is roughly 2.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 713 tokens, read only when the agent opens those files.

What are the alternatives to Segmentation Analysis?

Skills that share tags, products or a category with Segmentation Analysis: Exploratory Data Analysis (spacering-net/codeg, 3.8k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars), Exploratory Data Analysis (Oleafly/Oleafly, 206 stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Segmentation Analysis?

nimrodfisher (a GitHub user) maintains it in nimrodfisher/data-analytics-skills, which has 465 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on September 25, 2026.

Source: nimrodfisher/data-analytics-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.