Loading...
Loading...
Found 23 Skills
Customer interview question bank and synthesis templates for product discovery.
Help users conduct high-impact customer interviews that move beyond surface-level feature requests to identify root emotional frustrations and specific causal triggers.
Facilitates the final step of a proven customer-interview method: distilling everything a round of interviews produced (GOALS.md, HYPOTHESES.md, QUESTIONS.md, and a directory of per-interview debriefs) into a single FINAL-REPORT.md the whole company can use. Top: a summary as brief as possible without losing salient information. Below: numbered findings (F1, F2, …) tagged validated / disproved / directional / watch / untested, every one citing debriefs and quoting customers verbatim, plus per-area briefs that marshal the evidence for ideal-customer definition, positioning, pricing, marketing & sales, and product priorities. Load when the user says 'write up what we found from the interviews,' 'summarize the interview results for the team,' or 'turn the interviews into a report.' Do NOT load for updating hypotheses from interviews (the synthesis step), for recording one conversation (the debrief step), or for actually doing the positioning, ideal-customer, or pricing work the report feeds.
Facilitates the learning step of a proven customer-interview method — the synthesis half: reads a directory of per-interview debrief files against the working HYPOTHESES.md and QUESTIONS.md and proposes evidence-cited updates ONE at a time — double down, tune numbers, mark disproved, park heard-once observations in a 'That's funny' watch section, add new hypotheses with new questions — applying each agreed change directly to the files with a change log, and ending with a stop-or-continue-or-re-aim verdict on the interviewing itself. Load when the user says 'what did we learn from these interviews,' 'update my hypotheses from the interviews,' 'synthesize my interview notes,' or 'should I keep interviewing?' Do NOT load for recording a single conversation into a debrief (the previous step), for writing initial goals, hypotheses, or interview questions (earlier steps), or for deciding what to build next (comes after this method).
Facilitates the fifth step of a proven ideal-customer (ICP) method: mapping inciting events — the specific trigger moments that move a perfect-fit customer from could-buy-someday to buying-today. Takes a keystones file (K1, K2, … with market segments) and, when available, customer-interview findings; walks the keystones one at a time, harvesting real trigger stories from interview evidence (marked observed) and working backward through brainstorm lenses — crises, seasonal cycles, strategic windows, personal life-changes — for the rest (marked hypothesized), recording each event with the keystone it couples to and how to find prospects in that condition, in INCITING-EVENTS.md (E1, E2, …). Load when the user has keystones and asks what makes customers buy now, what triggers a purchase, or 'run the inciting-events step.' Do NOT load to derive keystones or deal-breakers (previous steps), to write the final ideal-customer definition (next step), or to write the ads themselves.
Facilitates the third step of a proven customer-interview method: translating hypotheses into open-ended, unbiased interview questions — each a miniature experiment designed to test one hypothesis without leading the witness. Two modes: given a single hypothesis, it grills the question into shape and outputs the final question in chat; given a HYPOTHESES.md file, it iterates the whole list, grouping related hypotheses, and maintains a QUESTIONS.md file (numbered Q1, Q2, … mapped to H-numbers) as a live, resumable artifact. Load when the user has hypotheses and wants interview questions, asks how to phrase a question for customers without biasing the answer, or says 'turn my hypotheses into questions' or 'help me ask about X without leading.' Do NOT load for writing goal questions or hypotheses (earlier steps), for conducting or analyzing the interviews themselves, for survey/questionnaire design, or for job interviews.
Facilitates the second step of a proven customer-interview method: recording the user's current best guesses — hypotheses — as numbered, falsifiable statements (H1, H2, …), each mapped to the goal questions it addresses, so interviews can confirm or contradict them instead of confirmation bias quietly filtering what's heard. Takes a GOALS.md goal-question list as input (file or pasted), elicits what the user believes goal by goal, sharpens vague beliefs into testable claims, prunes to hypotheses whose resolution would actually change what the user builds, targets, charges, or says, and preserves the result in HYPOTHESES.md. Load when the user has goal questions and wants to write hypotheses, list their assumptions, or record predictions before interviewing customers — 'I have my goals, what's next,' 'help me write down what I believe about my customers.' Do NOT load for writing the interview questions themselves, for analyzing interviews already conducted, or for statistical hypothesis testing.
Facilitates the first step of a proven customer-interview method: deciding exactly what you're trying to learn, written as numbered goal questions (G1, G2, …) that your hypotheses and interview questions will later be designed to answer. Interviews the user about their business — new idea or established company, B2B or B2C — then drafts a tailored goal-question list, critiques and revises it with them, and preserves the result in a GOALS.md file. Load when the user wants to interview customers, plan customer discovery or customer development, validate a startup idea, or answer unaskable questions like what to charge, how to position, who the ideal customer is, or what to build. Do NOT load for job or hiring interviews, for writing survey questionnaires, for analyzing customer interviews that have already been conducted, or when a goal-question list already exists and the user wants the next step (hypotheses).
Facilitates the learning step of a proven customer-interview method — the recording half: turning raw material from ONE customer conversation (transcript, notes, memory dump) into a brief per-person debrief file, mapped against the numbered interview-question list (Q1, Q2, … with H-number tags). One concise answer per question actually asked, key phrases kept verbatim, unasked questions honestly skipped, a one-line commentary only where in-the-moment context helps later analysis, and an addenda section for everything heard that fit no question. Load when the user has just done a customer interview and says 'here's the transcript,' 'process my call notes,' 'file this conversation against my questions,' or 'add this interview to the record.' Do NOT load for writing goals, hypotheses, or interview questions (earlier steps), for synthesizing patterns across multiple interviews and updating hypotheses (the next step), or for summarizing meetings or transcripts unrelated to customer interviews.
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.
Scores whether a business idea can become a viable business, walking the path from 'The Problem' to 'Viable Business Model': one specific target market is Fermi-scored on seven multiplying criteria (Plausible, Self-Aware, Lucrative, Liquid, Eager ×2, Enduring), every optimistic number is challenged unless evidence backs it, and when the verdict is bad, narrower niches are re-scored side by side. Load when the user asks whether their startup or product idea is viable, whether a market really exists for what they want to build, why a product that solves a real problem isn't selling, which of several target markets to pursue, whether to persevere or give up on a product, or says 'score my idea.' Do NOT load for general devil's-advocate stress-testing of plans or pitches without the viability question, for building a full ideal-customer profile, for writing customer-interview questions, or for execution problems like funnel optimization, hiring, or fundraising.
Guide product managers through preparing for customer discovery interviews by asking adaptive questions about research goals, customer segments, constraints, and methodologies. Use this to design effe