What problem does it solve? Product managers struggle to objectively rank competing feature requests and extract consistent insights from customer interviews. This Skill replaces gut-feel prioritization with quantitative RICE scoring and turns raw interview transcripts into structured pain points, feature requests, and jobs-to-be-done. ## Core Features & Use Cases - RICE Feature Prioritization: Score features by Reach, Impact, Confidence, and Effort from a CSV file, with portfolio analysis identifying quick wins versus big bets and capacity-based quarterly roadmap generation. - Customer Interview Analysis: Parse interview transcripts to extract pain points with severity, feature requests with priority, jobs-to-be-done patterns, sentiment scores, key themes, and competitor mentions. - PRD Templates: Four ready-to-use formats (Standard PRD, One-Page PRD, Agile Epic, Feature Brief) covering problem definition, requirements, success metrics, and go-to-market planning. - Use Case: A PM with 15 feature requests and a 10 person-month quarterly capacity runs the RICE prioritizer to produce a ranked list and suggested quarterly roadmap, then analyzes five user interview transcripts to validate which pain points the top features actually address. ## Quick Start Ask the AI to prioritize your feature backlog by running the RICE prioritizer script on a CSV of features, or to analyze a customer interview transcript file for pain points and feature requests.
