alaimo-labs/afpm

Analyze survey results — quantitative summary by learning goal, coded open-ends, and actionable insights extracted from the data

Present a fuzzy feature idea and get it clarified through evidence-grounded questioning, one question at a time — decisions stay yours, unknowns become assumptions

The Cognitive Friction Map (MFC) — four categories of cognitive friction (transformation, limiter, standardizer, evaluator points) for finding where AI adds real value in a user journey. Use when analyzing a journey or workflow for AI opportunities, identifying cognitive frictions or bottlenecks in user tasks, or deciding which steps of a process deserve an AI feature.

Have your synthetic personas critique a spec, PRD, or product idea — individual in-character reviews plus a panel synthesis

Derive evidence-based personas from patterns that recur across real interviews and survey results — bottom-up, every trait traceable to evidence

Design an interview guide for real user research from what you want to learn or validate — grounded in your insights, assumptions, and personas

Design a survey questionnaire from what you want to measure or validate — grounded in your insights and assumptions, ready to paste into any survey tool

How to build an Exposure Plan — an ordered set of accumulative reveal levels that validate a feature's hypothesis layer by layer, each level testing one falsifiable belief. Use when slicing a feature, planning a progressive reveal, designing how to validate a hypothesis in stages, or deciding what to expose to which users in what order.

Extract actionable product insights from one or more interview transcripts (synthetic or real)

Structure and quality bar for evidence-grounded feature specs — problem, user journey, critical user stories with acceptance criteria, and a falsifiable hypothesis. Use when writing a feature spec or PRD, defining user stories or acceptance criteria, mapping a user journey, or turning insights into a spec.

Frame an opportunity — a problem for a segment, backed by signals — before any solution is chosen. Evidence-grounded questioning, one question at a time; unknowns become beliefs, and the output is a research agenda, not a feature

Generate a diverse set of synthetic user personas for your product — typed primary/secondary/tertiary/negative, mix on request — saved as markdown files ready for interviews and critiques

How to extract actionable product insights from user interview transcripts (real or synthetic) — focus areas, quality bar, and output format. Use when analyzing interviews, synthesizing research, extracting insights, or turning transcripts and user feedback into product decisions.

How to design an interview guide (discussion guide) for real user research — from learning goals to open, non-leading questions, funnel structure, and probes. Use when designing an interview, writing interview questions, preparing a discussion guide, or planning research conversations with real users.

Interview one of your synthetic personas — exploration mode (open discovery) or validation mode (feedback on an idea)

Analyze a user journey step by step to identify cognitive frictions where AI could add real value (Cognitive Friction Map)

How to frame a product opportunity — a problem for a segment, backed by signals, with no solution chosen yet — and keep it apart from solutions. Opportunity vs. solution vs. outcome, signals vs. proof, the value and viability beliefs of an opportunity, the research agenda, and the Opportunity Solution Tree as a file layout. Use when framing an opportunity, deciding whether something is a problem or a solution, working with an opportunity solution tree, or when an idea arrives as a solution and you need the problem underneath it.

How to run a persona critique — a synthetic persona reviews a spec, PRD, or product idea in character and returns a structured rating with strengths, concerns, and suggestions. Use when critiquing a spec with personas, getting user feedback on a document, running a review panel, or stress-testing a PRD from the user's perspective.

Run secondary research / benchmarking on your product's market — competitors, alternatives, pricing, trends — with every claim labeled by provenance, mapped back to your unverified beliefs

Weekly evidence review — sweep the product/ artifacts created since the last review against the unverified beliefs in product/overview.md, propose belief-status annotations, report what's drifting, and log human corrections to AI proposals

How to do secondary research and benchmarking for product discovery — provenance discipline, source hierarchy, research lanes (competitors, alternatives, pricing, trends), and mapping findings back to your unverified beliefs. Use when researching a market, benchmarking competitors, sizing an opportunity, or gathering existing knowledge about a product space.

Turn a feature spec's hypothesis into an Exposure Plan — accumulative reveal levels that each test one falsifiable belief

Start product discovery — place the product on two axes (new/existing, commercial/internal) and capture its context plus a tagged, ranked list of unverified beliefs into product/overview.md, the context every other skill reads

How to design survey questionnaires and analyze their results — question types, wording bias, ordering, scales, quantitative summaries, and coding open-ended responses. Use when designing a survey or questionnaire, writing survey questions, choosing scales, or analyzing survey results and responses.

How to roleplay a synthetic persona in a product-discovery interview, with two modes — exploration (open discovery of pains and workflows) and validation (structured feedback on a specific idea or solution). Use when interviewing a persona, simulating a user interview, roleplaying a user, or running discovery/validation conversations with synthetic users.

How to create rich, diverse synthetic user personas (archetypes) for product discovery — structure, design principles, and diversity requirements. Use when creating personas, user archetypes, synthetic users, or when the user asks to model their target users, segments, or audiences.

Pretest an interview guide by running it against a synthetic persona — surfaces speculation, leading questions, dead ends, and coverage gaps in the guide, then revises it

Draft an evidence-grounded feature spec from your insights and personas — problem, user journey, critical user stories with acceptance criteria, falsifiable hypothesis