cac-modelerlisted

<!-- version: 0.4.0 | outputs: memory/ideas/<slug>/cac.json --> # Skill: cac-modeler ## Purpose Determine whether this indie developer can realistically acquire users profitably given their budget and the competitive landscape. Many ideas fail not because of bad products but because CAC exceeds LTV at indie scale. This skill builds a complete unit economics picture: LTV from retention and pricing, CAC from channel benchmarks calibrated by market_insights signals, and a payback timeline that tells the founder how long they need to fund growth before the business sustains itself. ## Input - Idea slug - `memory/ideas/<slug>/idea.md` (`business_model`) - `memory/user_profile.md` (budget constraint, ICP tier) - `memory/ideas/<slug>/pricing.json` (target price or primary tier → revenue per user; trial-to-paid or freemium conversion) - `memory/ideas/<slug>/retention.json` (D30 retention, churn risk → estimated lifespan) - `memory/ideas/<slug>/distribution.json` (viable channels, k-factor, ASO opportunity, creator fit) - `memory/ideas/<slug>/competitors.json` (competitor pricing and scale signals — optional) - `memory/market_insights/<niche>-*-<YYYY>-<MM>.md` (trend data — **use all available platform files**) ### Using Market Insights Trend analysis files provide critical calibration for CAC estimates. Extract the following: | Field | How it informs CAC | |---|---| | `trend_velocity` | Rising markets have lower organic CAC (more discovery demand) but higher paid CAC (more ad