retention-predictorlisted

<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/retention.json --> # Skill: retention-predictor ## Purpose Retention determines LTV. An app that churns users in week 1 can't build a business regardless of acquisition. This skill evaluates how sticky the idea is structurally — not based on feature lists, but on the underlying usage pattern and habit formation potential. ## Input - Idea slug - `memory/ideas/<slug>/idea.md` (app concept, `business_model`) - `memory/ideas/<slug>/desire_scores.json` (desire strength and primary driver inform habit potential) - Optional: `memory/ideas/<slug>/pricing.json` (pricing model affects commitment), `memory/ideas/<slug>/competitors.json` (incumbent churn signals from reviews) - `memory/market_insights/<niche>-*-<YYYY>-<MM>.md` (usage cadence and complaint patterns) ### Lane selection `business_model` = `b2c` or `prosumer` uses the B2C lane (D1/D7/D30 user retention). `b2b-smb` or `b2b2c` uses the B2B lane (monthly logo churn, cohort retention at month 1-2, 6 and 12). Both lanes fill `d30_equivalent` so idea-scoring can apply one rubric. ## Evaluation Factors | Factor | High Retention Signal | Low Retention Signal | |---|---|---| | Usage frequency | Daily or multiple times/day | Weekly or less | | External trigger | Clear real-world trigger (meal, workout, payday) | No natural trigger | | Progress/reward loop | Clear progress visible over time | No feedback loop | | Network effects | Gets better with more users | No network compo