What do I need to use Cohort Curve Model?

Observed retention by period; ARPU per period (optional); Projection horizon (optional).

Is Cohort Curve Model free to use?

Yes — run it free in the browser playground (no API key needed for the sponsored trial, or bring any provider key), or install it into Claude Code, Cursor, ChatGPT and more via npx pm-claude-skills. MIT-licensed and open source.

Cohort Curve Model

Q: How do I know the Cohort Curve Model output is good?

The skill verifies itself against its own quality checks: Period 0 is normalised to 100% and the input had at least 4 periods — otherwise the fit was refused, not fudged; R² is reported next to the projection, and a fit below 0.9 carries an explicit "distrust beyond the tail" warning; The b-parameter is interpreted in words (flattening / normal / leaky), not left as a naked number; LTV states its horizon — "LTV over 24 periods", never an unbounded number.

🔵 Stable🕐 updated 2026-07-06 🔷 SkillSpec L3 ⚙ ships an executable helper pm-calculators

Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Use when someone has real cohort retention numbers (month 0, 1, 2…) and asks what lifetime value, lifetime periods, or long-run retention they imply, or whether retention is flattening or leaking. Produces a fitted power curve (parameters, R², retention floor), a 24-36 period projection, and a real .xlsx with live formulas where editing ARPU recalculates LTV — via the bundled zero-dependency script.

▶ Run it free — no key needed 📝 Grade your existing draft View SKILL.md ↗

What to give it

▸Observed retention by period — from period 0 (100%) through at least period 3-4. Percent or fraction, either works. More periods = a trustworthy fit; 4 is the floor.

▸ARPU per period (optional) — (optional) — revenue per *retained* user per period. Without it, LTV is reported in lifetime-period multiples instead of currency.

▸Projection horizon (optional) — (optional, default 24 periods).

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

✓Period 0 is normalised to 100% and the input had at least 4 periods — otherwise the fit was refused, not fudged

✓R² is reported next to the projection, and a fit below 0.9 carries an explicit "distrust beyond the tail" warning

✓The b-parameter is interpreted in words (flattening / normal / leaky), not left as a naked number

✓LTV states its horizon — "LTV over 24 periods", never an unbounded number

✓The xlsx was actually generated by the script and the ARPU cell recalculates LTV

⚠️ What it refuses to do

Do not fit fewer than 4 periods — two points always fit a power law and mean nothing

Do not project a poor fit silently — a beautiful curve through bad residuals is how LTV fictions get funded

Do not quote LTV without the horizon — "lifetime" hides the assumption that matters

Do not average incomplete cohorts into the input (young cohorts drag the tail down mechanically — survivorship in reverse)

Do not present the fitted floor as a promise — it is an extrapolation, and the honest phrasing is "if the current shape holds"

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcp

Related skills

🔌 Embed this skill

Drop this on your blog, docs, or site — it renders a "Run this skill" card:

<div data-pm-skill="cohort-curve-model"></div>
<script src="https://mohitagw15856.github.io/pm-claude-skills/embed.js" async></script>

💬 Discussion

Cohort Curve Model is one of 599 open-source professional AI agent skills — all SkillSpec L3. Try them all in the browser · ⭐ Star on GitHub · Browse the full catalog