What problem does it solve? Research findings typically die in a readout deck, forcing teams to re-run studies that were already answered. This Skill turns each finding into an atomic, tagged, traceable nugget and governs the repository so insight compounds across projects instead of resetting every study. ## Core Features & Use Cases - Atomic nugget format: Every insight is stored as observation + 4-axis tags (theme, persona, product area, JTBD) + mandatory source, with confidence, evidence, decision link, and status fields. - Two governance gates: Gate 1 queries the repository before any new study to avoid re-research; Gate 2 files nuggets at the end of every synthesis so no finding goes unfiled. - Situational storage: Routes visual content to Figma/FigJam and written content to Docs/Markdown under one shared controlled tag taxonomy. - Use Case: Before recruiting for a new chatbot study, query the repo by persona and JTBD, find an existing nugget proving the stylist-trust barrier, and narrow the study to only the genuinely unknown questions. ## Quick Start Ask the AI to atomize the findings from your latest research synthesis into tagged, sourced nuggets and check the repository for existing evidence before planning the next study.
