---
title: "afpm"
description: "AI-First Product Manager skills: synthetic personas, persona interviews (exploration and validation), insight extraction, persona critique panels, and exposure plans."
canonical: https://agentpluginsdirectory.com/plugins/afpm
last-updated: 2026-10-04
---

# afpm
AI-First Product Manager skills: synthetic personas, persona interviews (exploration and validation), insight extraction, persona critique panels, and exposure plans.
- Slug: afpm
- Publisher: Alaimo Labs
- Repository: https://github.com/alaimo-labs/ai-first-skills
- Manifest: afpm/plugin.json
- Version: 0.11.0
- License: CC-BY-SA-4.0
- Category (editorial): research
- Skills: 30 (analyze-survey, clarify-idea, cognitive-frictions, critique-spec, derive-personas, design-interview, design-survey, explore-solutions, exposure-plans, extract-insights, feature-specs, frame-opportunity, generate-personas, insight-extraction, interview-guides, interview-persona, map-frictions, opportunity-framing, persona-critique, research-market, review-evidence, secondary-research, slice-feature, solution-exploration, start-product, survey-design, synthetic-interviews, synthetic-personas, test-interview-guide, write-spec)
- MCP servers: 0
- Stars: 13
- Repository created: 2026-07-12
- Repository last pushed: 2026-09-28
- Publisher type: Organization
- Listing: https://agentpluginsdirectory.com/plugins/afpm
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What afpm does, in the publisher's words

Agent skills for discovery and validation with synthetic users. Companion plugin for the AI-First Product Manager program by Alaimo Labs.

This plugin gives your coding agent a product-discovery toolkit: create synthetic personas, interview them, extract insights, run critique panels over your specs, and slice features into exposure plans. It also bridges to real research: design interview guides and surveys, analyze the results, and derive evidence-based personas from the patterns. All artifacts are plain markdown files in your repo under product/, no external services.

Content is written in English; all deliverables come out in the language you work in.

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/alaimo-labs/ai-first-skills/HEAD/afpm/README.md

## Skills

- analyze-survey: Analyze survey results: quantitative summary by learning goal, coded open-ends, and actionable insights extracted from the data
- clarify-idea: Present a fuzzy feature idea and get it clarified through evidence-grounded questioning, one question at a time, decisions stay yours, unknowns become assumptions
- 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-survey: 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
- explore-solutions
- 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…
- 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…
- frame-opportunity
- generate-personas: Generate a diverse set of synthetic user personas for your product, 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
- interview-persona
- map-frictions
- opportunity-framing
- persona-critique
- research-market
- review-evidence
- secondary-research
- slice-feature
- solution-exploration
- start-product
- survey-design
- synthetic-interviews
- synthetic-personas
- test-interview-guide
- write-spec

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.