Measure fit

Skill TimboGP/timbogp-marketplace/plugin/lean-coach/skills/measure-fit

A Claude Code plugin that turns the agent into a structured tutor over your own materials — per-topic sub-projects, curricula, and bracketed study sessions, with domain overlays. Domain-agnostic.

Install

npx -y skills add TimboGP/timbogp-marketplace --skill measure-fit

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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What its author says it does

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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.

SKILL.md

4.4 KB, as published. Nobody here has run it

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.

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.