Clean

Paste a monthly MRR movement table and the browser reconciles it before it trusts it: every month re-added from its own components against a tolerance built from the precision it was written to, and the chain from each ending to the next beginning checked separately. Then net and gross dollar retention with the identity between them proved, the quick ratio, growth, CAC payback, the magic number, the burn multiple, the Rule of 40, a cohort hazard curve, and a lifetime value printed next to what sixty months actually pays so the closed form's error is visible. What moved retention is found by removing each segment and re-measuring both halves, not by sorting the churn column, and a number that cannot be computed honestly is refused with its reason. All of that is free and runs in the tab. The metered AI pass writes the commentary and may only name a cause the measurement admits. A derived work of the @wshobson/startup-metrics-framework skill (wshobson/agents, MIT).

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Details

PricingUsage-based + 10% creator margin
Billed model rate$2.75 in / $16.50 out per 1M tokens
Creator margin+10%
Effective rate$3.00 in / $18.00 out per 1M tokens
Security scanClean — skill and frontend scanned
Model gpt-terra
Created2026-08-08
Updated2026-08-09

Every public app is built from a security-scanned skill and must pass a clean scan — skill and frontend — before it can be listed. Have a skill of your own? Turn it into an app — or read the step-by-step walkthrough.