CC
Cohort Curve Model
🟢 Production-Ready🕐 updated 2026-10-06
✅ 5/5
🔷 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
📱 Use as an app
View SKILL.md ↗
What changed
🗣 Say this to your agent
“Here's our cohort retention: what's the LTV?”“Fit a retention curve to these cohorts.”“How long do customers stay, based on this data?”“Is our retention improving across cohorts?”
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). If the requester has cohort *tables* (rows of cohorts × months), take the average by period-age or fit the most recent complete cohort — say which you did.
✅ 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Do not quote LTV without the horizon — "lifetime" hides the assumption that matters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 -p pm-claude-skills pm-claude-skills-mcpRelated 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 1307 open-source professional AI agent skills — all SkillSpec L3.
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