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persona-development

Generate JTBD-grounded user personas with AI-embracement segmentation in Markdown.

70|34|Updated Apr 7, 2026
Search Tags:#persona#segmentation#user-research#jtbd#user-profiles#ai-adoption
One-click install
npx skills add https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill persona-development-productfculty-aipm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: persona-development
Source: https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty/tree/main/skills/persona-development
Command: npx skills add https://github.com/Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill persona-development-productfculty-aipm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Product teams often create personas that are demographic checklists and do not change product decisions. This Skill turns product context and research into JTBD-grounded, decision-driving personas that clarify when users will choose your solution and what will make them switch.

Core Features & Use Cases

  • Context-aware persona synthesis: Loads product memory and existing persona notes to avoid duplicating work and surface gaps.
  • JTBD framing and decision triggers: Crafts triggering situations, jobs-to-be-done, current hires, pains, gains, and switch triggers to make personas actionable.
  • AI-embracer segmentation & anti-persona: Labels personas as AI Embracer / Neutral / Skeptic and produces an anti-persona to prevent over-targeting.
  • Team-ready outputs: Produces 2–3 persona profiles plus a one-page cheat sheet and offers to save to product context and memory files.

Quick Start

Create two JTBD-grounded personas and an anti-persona using product memory and any available research notes.

Frequently Asked Questions about persona-development

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create JTBD-driven user personas from research data?▼

To create JTBD-driven user personas, synthesize interview, survey, or support data into profiles containing triggering situations, current hires, pains, gains, and switch triggers. This process converts raw research into actionable outcome-focused user profiles.

What is an anti-persona and why do I need one for product segmentation?▼

An anti-persona defines users you should explicitly avoid targeting to prevent over-segmentation. Generating an anti-persona alongside 2-3 regular user profiles clarifies who your product is not built for, keeping product decisions focused.

How do I segment users by AI embracement for product development?▼

Segment users by AI embracement by labeling user profiles as AI Embracer, Neutral, or Skeptic. This segmentation clarifies adoption barriers and tailors product outcomes based on different user groups' readiness to adopt AI features.

Can I build actionable user profiles if I only have product context and no survey data?▼

Yes, you can build actionable user profiles by reading product memory and context files to synthesize available information. The process generates 2-3 persona profiles and a one-page cheat sheet even with limited research inputs.

Why do traditional demographic personas fail to change product decisions?▼

Traditional demographic personas fail because they act as checklists rather than mapping user needs. JTBD-grounded personas solve this by clarifying triggering situations and switch triggers, directly linking user research to actionable product decisions.

What is the best way to generate a one-page persona cheat sheet for my product team?▼

The best way to generate a one-page persona cheat sheet is by synthesizing product context and research into JTBD-framed profiles. This outputs 2-3 personas and an anti-persona in Markdown, ready to save to product memory files.

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