Amplitude 4-Quadrant Feature Portfolio & Retention Smile Curve Analytics

This skill exists to stop: judging features by gut feel or the loudest voice in the room, instead of their real position on the breadth × frequency matrix.

📁 Source note: [sage] = upstream Sage repo (github.com/xoai/sage, public) — optional deeper reading; this skill runs fully on the rules inlined here. A step marked MUST READ points at a file in your own project (e.g. an event registry) — if it is missing, stop and ask instead of improvising.

🤖 0. HOW TO USE (agent workflow)

A. Build the 4-quadrant matrix from MAU/frequency data (median split), with measurement window + source. B. Investigate per quadrant: Core → verify value/reliability; Power-Niche → assess wider relevance; Casual-Broad → check natural usage cadence; low-use corner → investigate customer value, discovery, and dependencies. C. Smile curve: plot cohorts, mark the inflection; never conclude from incomplete cohorts. Standard output: matrix + evidence + one investigation or experiment per feature, with owner and review trigger. A quadrant alone never authorizes deletion; first establish customer impact, contractual/accessibility needs, dependencies, and a safe migration or replacement.

🧭 1. Core Framework: T-Shaped Retention vs Engagement