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Found 6 Skills
Data-driven customer persona development combining market research, user behavior analysis, and segmentation frameworks. Use when creating buyer personas, ideal customer profiles (ICPs), or user archetypes.
Comprehensive AB testing analysis tool that supports experimental design, statistical testing, user segmentation analysis, and visual report generation. It is used to analyze AB test results such as product revisions, marketing campaigns, and function optimizations, providing statistical significance testing and in-depth insights.
Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.
Creates customer and user segment documents. Use when the user wants to 'segment customers,' 'customer segments,' 'user segments,' 'lifecycle segments,' or 'audience segmentation.'
Take one or many customer-interview files you already have and extract the AJTBD structure from them — segments by Core Jobs, personas, Consideration Set, existing Solutions and Problems, value hypotheses — using Ivan Zamesin's AJTBD / Next Move Theory methodology (distinct from generic Christensen JTBD). Input — a folder or list of files: deep-interview transcripts, interview notes, sales-call or demo transcripts, support/chat logs, survey open-ends. The interviews may be AJTBD or not, well- or poorly-conducted, one file or dozens. The skill first asks which business task you're solving (and helps you choose if you can't name one), then reads each interview in its own subagent (a fan-out so it never overflows context, no matter how many large transcripts), extracts the Core Jobs with an honest per-interview confidence (a clean extraction vs. a weak hypothesis), gives per- interview feedback (what was pulled, what's missing, whether this interview can even serve your business task), clusters the extractions into segments by similar Core Jobs + similar success criteria + similar priority order, and computes each segment's confidence from the supporting interviews' confidence. Output — one report: a data-quality summary, segments by Core Jobs with personas and confidence, structured existing Solutions and Problems, a Consideration Set per segment, value-creation hypotheses, and a gap list of what to interview next. Use when the user says "analyze my interviews", "extract jobs from these transcripts", "I have customer interviews — find the segments", "what jobs are in these calls", "synthesize my interviews", or has interview/transcript files and wants the methodology pulled out of them. The post-fieldwork counterpart to /nmt-interview-guide. Two modes — Quick (default, no internet) and Deep (subagents + web to enrich competitors and the Consideration Set). Plain language; defaults to English.
Use when needing to transform followers into a structured community ecosystem with clear user journeys, value delivery systems, and sustainable growth mechanisms