jtbd-extractorlisted

# JTBD Extractor Turn raw research into Jobs-to-be-Done statements showing what users are really trying to accomplish - reframing feature requests as underlying needs and uncovering innovation opportunities. ## Output Save to `discovery/outputs/jtbd-[persona]-[YYYY-MM-DD].md` ## When to Use This Skill - Reframing feature requests into underlying needs - Finding innovation opportunities in saturated markets - Training your team to think in jobs, not features ## What You'll Get I'll generate a complete Jobs-to-be-Done analysis with: - **JTBD statements** - Properly formatted "When [situation], I want to [action], so I can [outcome]" - **Job categories** - Separated into Functional, Emotional, and Social jobs - **Opportunity scores** - Importance + (Importance - Satisfaction) to show where to focus - **Evidence tracking** - Direct quotes and behavior supporting each job - **Feature translation** - Mapping what users asked for to what they actually need - **Top opportunities** - Ranked list of underserved jobs worth solving ## What You'll Need - Interview transcripts, survey responses, or customer feedback - Context on your product/market ## Process ### Step 1: Review Your Context I'll start by checking your context files to understand what you already know: - **personas.md** - What jobs do your existing personas have? - **product.md** - What problems does your product solve today? - **Research files** - Any prior interview snapshots or feedback data? I'll share what I