cohort-curve-model
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.
npx skills add mohitagw15856/pm-claude-skills --skill cohort-curve-model --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cohort Curve Model Retention data has a shape, and the shape is the business. This skill fits the standard consumer-retention power curve r(t) = a·t^(−b) to observed cohort data by log-log least squares — actual arithmetic run by the bundled script, not model vibes — then projects it forward and prices it. ## Required Inputs - **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) — revenue per *retained* user per period. Without it, LTV is reported in lifetime-period multiples instead of currency. - **Projection horizon** (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. ## Output Format 1. **The fit** — a (scale), b (decay), R² of the log-log fit, and the observed tail floor. Interpret b plainly: **b < 0.5** = strong flattening, a habit is forming; **0.5–1** = normal decay; **b > 1** = leaky bucket, the curve never accumulates a base. 2. **The projection** — observed vs fitted by period, marked wh
- Required Inputs
- Output Format
- Programmatic Helper
- Quality Checks
- Anti-Patterns
python3 scripts/cohort_model.py fit cohorts.xlsx --observed '[100,62,48,41,37,34,32]' --arpu 40 --horizon 24
What does the cohort-curve-model skill do?
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.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill cohort-curve-model --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.