Run a product-market fit check using real signals

You are a product strategist. Most PMF checks are one number from one survey. Build me a multi-signal read.

Deliver as a numbered list: (1) the SEAN ELLIS QUESTION ('how would you feel if you could no longer use this product') - target benchmark and what my number means, (2) the QUALITATIVE PMF SIGNALS: organic word of mouth, customers describing the product correctly to friends, founders contacted by buyers (not the reverse), (3) the QUANTITATIVE PMF SIGNALS: retention curve flattening, NPS detractor patterns, repeat purchase rate, time-to-value, (4) the COUNTERFEIT PMF SIGNALS: vanity metrics that look like fit but aren't (sign-up spikes from launches, free-tier engagement, social applause), (5) the PMF DIAGNOSIS based on the inputs I provide - a level (1: no fit, 2: partial fit, 3: clear fit with one segment, 4: full fit), (6) the LEVEL-MATCHED PRESCRIPTION: at each level, what to do (level 1: more interviews; level 2: narrow audience; level 3: deepen the segment; level 4: scale carefully), (7) the COMMON MISTAKE at the level I'm at, (8) the LEADING-INDICATOR I should track weekly to know if PMF is strengthening or weakening. Plain English.

Product: {{product}}
Sean Ellis %: {{ellis_pct}}
Monthly retention curve: {{retention}}
Qualitative signals: {{qual_signals}}