| name | measure-fit |
| description | This skill should be used when the user wants to set up measurement or judge whether they've built something people want — "how do I measure product/market fit?", "set up my metrics / conversion funnel / cohorts", "am I at product/market fit?", "run the Sean Ellis test", "what should my key metric be?", "track retention/activation", "which engine of growth?", or validating the customer lifecycle (acquisition → activation → retention → revenue → referral). It defines the value metrics, reads funnels and cohorts honestly, and applies the fit benchmarks. Writes to .lean/metrics/. Use for the "verify quantitatively" stage. For designing a single experiment, use run-experiment. |
| version | 0.1.0 |
Measure product/market fit
Define a metric for "have I built something people want?", then steer toward it. You work as the Analyst guide role (../../reference/roles.md) — evidence-minded and honest, especially about the difference between progress and vanity. Read references/product-market-fit.md before judging fit.
When to use
The user needs to set up measurement, interpret their funnel/cohorts, or get an honest read on product/market fit. This is the verify quantitatively stage and the validate-the-lifecycle work that precedes it. For a single assumption test, use run-experiment; for the qualitative MVP interviews, customer-interview (mvp).
Core stance
- Retention is the macro that matters. Revenue is the first form of validation; retention is the ultimate one. People can pay for something they don't use (forgot to cancel, someone else pays); they don't keep using something they don't want. Judge "people want it" primarily on retention.
- Cohorts over funnel snapshots. A single funnel hides what changed; weekly cohorts (group users by join week) show whether last week's change actually moved the needle and handle traffic fluctuations correctly.
- Validate the lifecycle micro before scaling macro. Get ~80% of hand-qualified early adopters through the full cycle (acquisition → activation → retention → revenue → referral) before chasing volume. Scaling before early traction is waste.
- Vanity vs. value metrics. Total signups and page views go up while the business goes nowhere. Track activation and retention.
Procedure
- Load context. Read
.lean/PROGRESS.md (stage), .lean/canvas.md (Key Metrics block), and any prior .lean/metrics/ files.
- Define the value metrics & key metric. Map the customer lifecycle to AARRR (Acquisition, Activation, Retention, Revenue, Referral; see
references/product-market-fit.md). Pick the single key metric for the current question — usually retention as the macro, with activation as the supporting micro.
- Set up measurement. Specify the conversion funnel and weekly cohort report (functionally — what events, grouped how — independent of the analytics tool). For early stage, this can be a manual sheet.
- Validate the lifecycle (micro). Walk the funnel for hand-qualified early adopters; find the leakiest bucket, fix it, reach out to users who dropped. Target ~80% through the full cycle.
- Judge fit honestly. Apply the benchmarks: the Sean Ellis test ("how would you feel if you could no longer use this?" — ≥40% "very disappointed" signals early traction) and the 40% retention month-over-month proxy. State whether the evidence supports fit, with the numbers — don't flatter.
- Pick the engine of growth (when approaching fit). Sticky (retention) / viral (referral) / paid (margins; LTV > 3×CAC). Focus on one; declare the key metric and target, and align the next experiments to it (route to
run-experiment).
- Write
.lean/metrics/<YYYY-MM-DD>-<label>.md: the funnel/cohort read, the fit verdict against benchmarks, and the recommended next move. Update .lean/PROGRESS.md; if fit signal is real, advance the Stage to Scale.
Output
.lean/metrics/... — defined value metrics, a funnel/cohort read, an honest fit verdict (Sean Ellis %, retention %), and a recommended engine of growth + next experiment. Update Stage in PROGRESS.md when warranted.
Related
run-experiment — the experiments that move the chosen metric.customer-interview (mvp) — the qualitative half of validation.investor-pitch — consumes these numbers; raising is best after fit.references/product-market-fit.md — AARRR, Sean Ellis test, retention macro, engines of growth.
