TypingMindTypingMindThe best frontend for AI
Cohort Analysis logo

Cohort Analysis

Community
nimrodfisher
cohort-analysis

Time-based cohort analysis with retention and behaviour tracking. Activate when you need to measure how groups of users/customers behave over time — retention rates, revenue by cohort, or feature adoption curves.

Overview

Publishernimrodfisher
Repositorydata-analytics-skills
Skill namecohort-analysis
Stars
493
Forks
87
Bundled files
8
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 8 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 Cohort Analysis 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.
    https://github.com/nimrodfisher/data-analytics-skills/tree/main/03-data-analysis-investigation/cohort-analysis
  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/nimrodfisher/data-analytics-skills.git /tmp/data-analytics-skills
mkdir -p .claude/skills
cp -r /tmp/data-analytics-skills/03-data-analysis-investigation/cohort-analysis .claude/skills/cohort-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cohort 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 Cohort 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 Cohort 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.

When to use

  • A stakeholder asks "are we retaining users better than last quarter?"
  • You need to measure N-day, weekly, or monthly retention for a product or feature
  • You want to compare how different acquisition cohorts (by channel, plan, or signup date) perform over their lifetime
  • You're investigating churn and need to identify at which period users typically leave

Process

  1. Define the cohort and activity — clarify: cohort grouping (signup month, first purchase date, etc.) and retention event (login, purchase, feature use). Document in the report header.
  2. Pull or build the data — if starting from a database, use scripts/cohort_query.sql as the starting point. Adapt the cohort_date and activity_date columns to your schema.
  3. Build the cohort table — run scripts/cohort_builder.py to produce a cohort × period membership table from event data. Output is a CSV with user_id, cohort_period, activity_period.
  4. Compute the retention matrix — run scripts/retention_matrix.py on the cohort table to generate the period-over-period retention rates. Output is an N×M matrix (cohort × period).
  5. Visualise — run scripts/cohort_visualizer.py to render a heatmap of the retention matrix and a time-series of retention curves per cohort.
  6. Interpret findings — consult references/retention_metrics_glossary.md for metric definitions and references/cohort_definition_patterns.md for pattern recognition.
  7. Write the report — fill assets/cohort_report_template.md. For a visual deliverable, fill in the assets/retention_matrix.html heatmap template.

Inputs the skill needs

  • Required: event data with user_id, cohort_date (e.g. signup_date), activity_date
  • Required: cohort grouping granularity (daily / weekly / monthly)
  • Required: retention event definition — what counts as "active" or "retained"?
  • Optional: minimum cohort size (recommend ≥ 100 users; smaller cohorts have noisy rates)
  • Optional: number of periods to track (e.g. 12 months)
  • Optional: cohort attributes to segment by (acquisition channel, plan tier, geography)

Output

  • assets/cohort_report_template.md (filled) — narrative interpretation and retention figures
  • assets/retention_matrix.html (filled) — colour-coded retention heatmap
  • scripts/retention_matrix.py output CSV — raw retention rates for downstream use

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 Cohort Analysis AI skill do?

Time-based cohort analysis with retention and behaviour tracking. Activate when you need to measure how groups of users/customers behave over time — retention rates, revenue by cohort, or feature adoption curves.

Why use Cohort Analysis on TypingMind?

Because you install it once and use it with any model. Cohort 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 Cohort 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/cohort-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 Cohort 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 Cohort Analysis?

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

Is the Cohort 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.

Related AI Skills

View all
openclaw logo

Notcrawl

openclaw
OrganizationPopular

Notion archive: search, sync freshness, pages/databases, Markdown exports, SQL counts, and Notcrawl repo work.

82.2K
391K
1 files
View
openclaw logo

Openclaw Test Heap Leaks

openclaw
OrganizationPopular

Investigate OpenClaw pnpm test memory growth, Vitest OOMs, RSS spikes, and heap snapshot deltas.

82.2K
391K
2 files
View
openclaw logo

Prototype Openclaw Tui

openclaw
OrganizationPopular

Build throwaway, fixture-driven OpenClaw Clack or Pi TUI prototypes and compare multiple interactive variants side by side in tmux without running the full application or touching live state.

82.2K
391K
2 files
View
openclaw logo

Slacrawl

openclaw
OrganizationPopular

Slack archive: search, sync freshness, threads/DMs, SQL counts, and Slacrawl repo work.

82.2K
391K
1 files
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇