"data-cohort-analysis"

"data-cohort-analysis" is a skill for Claude Code from charlieviettq/awesome-agent-skill. It costs 92 tokens per session (1,205 once invoked), scanned A, a copy of data-cohort-analysis, MIT.

A method for comparing groups of users over time, such as people who signed up in the same month, using retention tables and behavior trends.

In plain words
What is it for?
Use it to measure retention, compare product versions or acquisition periods, study engagement, and estimate customer lifetime value.
Why use it?
Overall averages can hide the fact that newer or older user groups are behaving differently.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it to measure retention, compare product versions or acquisition periods, study engagement, and estimate customer lifetime value.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/charlieviettq/awesome-agent-skill/data-cohort-analysis
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add charlieviettq/awesome-agent-skill --skill data-cohort-analysis
Clone the repo
git clone --depth 1 https://github.com/charlieviettq/awesome-agent-skill

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for "data-cohort-analysis"

README.md
[![agentmods](https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis/github.svg)](https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis)
Your own site
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for "data-cohort-analysis"

Your own site · 80×15
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,205 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 86% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00092 $0.01205
Opus 5 $0.00046 $0.00602
Sonnet 5 $0.00018 $0.00241
Haiku 4.5 $0.00009 $0.00120

Measured 9d ago against content hash 69c2cfd6da9e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-13, from the pricing page.

Security

Grade A, and why

"data-cohort-analysis" scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

86% identical to data-cohort-analysis — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/skills/data-cohort-analysis/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cohort Analysis

Framework

IRON LAW: Aggregate Metrics Hide Cohort Differences

A 70% monthly retention rate OVERALL can mask that January cohort retains
at 85% while June cohort retains at 50%. Aggregate metrics blend improving
and deteriorating cohorts together, hiding both problems and progress.
ALWAYS analyze by cohort before drawing conclusions.

Core Concepts

Cohort: A group of users who share a common characteristic in a specific time period. Most common: acquisition cohort (grouped by signup month).

Retention Matrix: Rows = cohorts (by signup month), Columns = time periods after signup (Month 0, 1, 2...). Cells = % of cohort still active.

           Month 0  Month 1  Month 2  Month 3
Jan cohort   100%     65%     48%      40%
Feb cohort   100%     60%     42%      35%
Mar cohort   100%     70%     55%      48%  ← Improvement!

Retention Types

Type Definition Use Case
N-day % active on exactly day N Games, daily-use apps
N-day bounded % active within first N days General product usage
Week/Month % active in week/month N SaaS, subscriptions
Unbounded % who ever return after day N Low-frequency products

Analysis Steps

Phase 1: Define Cohort and Activity

  • Cohort definition: signup date, first purchase date, or other milestone
  • Activity definition: login, purchase, specific action — must match the product's core value
  • Time granularity: daily (for daily-use products), weekly, or monthly

Phase 2: Build Retention Matrix

  • Group users into cohorts
  • For each cohort, calculate retention at each time period
  • Visualize as a heatmap (darker = higher retention)

Phase 3: Identify Patterns

  • Retention curve shape: Does it flatten (good — stable core users) or keep declining (bad — everyone eventually churns)?
  • Cohort comparison: Are newer cohorts retaining better or worse than older ones?
  • Drop-off cliff: Is there a specific period where retention drops sharply? (e.g., Day 1 → Day 7 drops 50%)

Read the full file on GitHub · 110 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 110 lines · 92 tokens per session scan A 69c2cfd6da9e

Subscribe to this mod's changes

"data-cohort-analysis" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,205 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to data-cohort-analysis, differing in 8 lines, and is treated as a copy.

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