AI-First Product Manager

Plugin afpm from marketplace ai-first-skills in alaimo-labs/ai-first-skills. Commands read from the repo's manifest — 16 days ago.

Skills (41)

• analyze-feedback

  Analyze post-launch feedback (reviews, tickets, in-app survey comments, CS notes, social mentions) — tag each entry, group into themes with counts, severity and verbatims, and, when a metrics analysis exists, say which of its hypotheses the feedback supports or contradicts. Works on its own too

• analyze-metrics

  Compute the four AARS metrics of a feature from usage data with the definitions in its metrics file, plus the funnel and the retention curve, mark which metric is below target, and propose ranked hypotheses for why. Metrics are designed with /design-metrics; feedback is read next with /analyze-feedb

• analyze-solution-tests

  Analyze the results of solution tests against the thresholds set before they ran, decide per alternative whether it continues, changes, or is discarded, and update the tests file, the solutions file, and the belief registry with approval. Tests are designed with /design-solution-tests

• analyze-survey

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

• build-solution-test

  Build the material for one solution test already designed with /design-solution-tests — a prototype, a prompt with its input set and answer key, a facilitation script, a fake-door page, a capture sheet — exactly what the design asks for to produce its signal, and nothing more

• clarify-idea

  Starting from one idea already chosen (a solution, not a problem), get it clarified through evidence-grounded questioning, one question at a time — decisions stay yours, unknowns become assumptions. To compare alternatives for a problem first, use /explore-solutions

• cognitive-frictions

  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

• critique-spec

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

• derive-personas

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

• design-interview

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

• design-metrics

  From a spec and its exposure plan, define the four AARS metrics of a feature (acquisition, activation, retention, satisfaction) over the target audience — signal, expanded definition, signal threshold, calculation, target, and the events to instrument — before the level is revealed. Data is read lat

• design-solution-tests

  Starting from a solutions file, design the cheapest test that could refute the riskiest belief of each alternative under test, before anything is built — what to do, with whom, the signal, the threshold, and the decision for each result. Material is built with /build-solution-test; results are analy

• design-survey

  Design a survey questionnaire from what you want to measure or validate — grounded in your insights and assumptions, with a distribution plan (named channels, one link each, target n, dates), ready to paste into any survey tool

• explore-solutions

  Starting from one opportunity (a problem, not an idea), generate 3 to 5 genuinely different solution alternatives, evaluate each against the evidence with the desirable / feasible / viable filter, and help the user choose which one moves forward. Ends with the value proposition of the chosen alterna

• exposure-plans

  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 t

• extract-insights

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

• feature-specs

  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

• feedback-analysis

  How to analyze post-launch feedback — support tickets, store reviews, in-app survey comments, sales and Customer Success notes, social mentions — the bias of each source, tagging every entry, grouping into counted themes with severity and verbatims, weak signals, and checking the hypotheses of a met

• frame-opportunity

  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-personas

  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

• insight-extraction

  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.

• interview-guides

  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 user

• interview-persona

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

• map-frictions

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

• opportunity-framing

  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 Soluti

• persona-critique

  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

• product-metrics

  How to measure a revealed feature with the four AARS metrics — acquisition, activation, retention, satisfaction — each a percentage of the target audience, with an observable signal, an expanded definition, a justified signal threshold, a target reconciled with the exposure plan, and the events to i

• research-market

  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

• review-evidence

  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

• secondary-research

  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 opportu

• slice-feature

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

• solution-exploration

  How to explore and compare solution alternatives for one opportunity before committing to one — what makes an alternative genuinely different, the current workaround as the baseline, the desirable / feasible / viable filter with who owns each judgment, the value proposition of the chosen alternative

• solution-testing

  How to test a solution alternative before building it — problem evidence vs. solution evidence, the cheapest test that could refute the riskiest belief, the test menu by risk (concierge, Wizard of Oz, prototype shown, usability test, technical test, fake door, buyer conversation), thresholds and dec

• stakeholder-critique

  How to have a stakeholder of the company that builds the product — CEO, product leadership, engineering, marketing, PR, legal and privacy, sales, customer success, finance, the sponsor — read an internal document (update, brief, results report, spec) in character and react as they would in the meeti

• stakeholder-review

  Have a stakeholder of the company that builds the product (CEO, legal, marketing, engineering, sales, the sponsor) read an update, brief, or results report in character and say what they would ask in the meeting, what they miss to decide, what worries them, and whether they would approve, ask for mo

• start-product

  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

• survey-design

  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.

• synthetic-interviews

  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

• synthetic-personas

  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.

• test-interview-guide

  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

• write-spec

  Draft an evidence-grounded feature spec from your insights and personas — or from a tested alternative in a solutions file — problem, user journey, critical user stories with acceptance criteria, falsifiable hypothesis

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