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Develop persona segment

Skill alexe-ev/product-plugins/product-discovery/skills/develop-persona-segment

Skill library for AI agents — 15 product domains, 121 skills. Tells the agent what to ask, how to reason, and what to output.

Install
npx -y skills add alexe-ev/product-plugins --skill develop-persona-segment

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Build and validate user personas and segment definitions grounded in real research data. Use this skill when a team needs to define who they are building for with enough specificity to guide product decisions.

SKILL.md

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Develop Persona & Segment

Purpose

Help teams build research-grounded personas and segment definitions that are specific enough to guide product, design, and messaging decisions.

Skill type

Conceptual skill

Use this skill when

  • The team doesn't have clear segments or has overly broad ones
  • Personas exist but are based on assumptions, not research
  • A new product area requires understanding a new user type
  • Segment prioritization decisions need clearer user definitions

Do not use this skill when

  • The segment is already well-defined and validated
  • The goal is quantitative segmentation modeling (use data-analytics skills)

Required inputs

  • Available research data or user observations
  • Product area or context

Optional inputs

  • Existing personas or segment definitions
  • Behavioral data (usage patterns, feature adoption)
  • Demographic or firmographic data (for B2B)

Upstream context

Works best when:

  • Qualitative research has been synthesized
  • At least some user interviews or feedback exist

If upstream context is missing

If no research data exists, produce a hypothesis persona clearly labeled as assumption-based. List what research is needed to validate it.

Downstream handoff

Output can feed:

  • frame-insight-opportunity
  • formulate-experiment-hypothesis (target segment)
  • develop-positioning-messaging

Instructions

  1. Identify distinct user types emerging from research data.
  2. For each type, define: goals, jobs to be done, pain points, context of use.
  3. Add behavioral characteristics if data supports it.
  4. Avoid demographic stereotyping — anchor in behaviors and motivations.
  5. Assess which segments are most strategically important.
  6. Flag which elements are research-backed vs. assumed.

Output

For each persona / segment:

  • Name and archetype
  • Primary goals and jobs to be done
  • Key pain points
  • Context of use
  • Behavioral patterns (if data supports)
  • Strategic priority for the product
  • Evidence quality: assumed / data-informed / validated
  • Research gaps

Risks / caveats

  • Do not build personas from demographic assumptions
  • A persona based on one interview is a hypothesis, not a finding
  • Avoid persona sprawl — 2–4 well-defined segments are better than 10 vague ones

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