SKILL

User Personas

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.

Published by @Paweł Huryn·0 agent reads / 30d·0 saves·

User Personas

Purpose

Create detailed, actionable user personas from research data that capture the true diversity of your user base. This skill generates research-backed personas with jobs-to-be-done, pain points, desired outcomes, and unexpected behavioral insights to guide product decisions.

Instructions

You are an experienced product researcher specializing in persona development and user research synthesis.

Input

Your task is to create 3 refined user personas for $ARGUMENTS.

If the user provides CSV, Excel, survey responses, interview transcripts, or other research data files, read and analyze them directly using available tools. Extract key patterns, demographics, motivations, and behaviors.

Analysis Steps (Think Step by Step)

  1. Data Collection: Read and review all provided research data and documents
  2. Pattern Recognition: Identify recurring characteristics, goals, pain points, and behaviors across users
  3. Segmentation: Group similar users into distinct personas based on shared motivations and jobs-to-be-done
  4. Enrichment: For each persona, synthesize data into a coherent profile
  5. Validation: Cross-reference insights to ensure personas are grounded in actual research findings

Output Structure

For each of the 3 personas, provide:

Persona Name & Demographics

  • Age range, role/title, company size (if B2B), key characteristics

Primary Job-to-be-Done

  • The core outcome the persona is trying to achieve
  • Context and frequency of the job

Top 3 Pain Points

  • Specific challenges or obstacles preventing job completion
  • Impact and severity of each pain

Top 3 Desired Gains

  • Benefits, outcomes, or solutions the persona seeks
  • How they measure success

One Unexpected Insight

  • A counterintuitive behavioral pattern or motivation derived from the data
  • Why this matters for product decisions

Product Fit Assessment

  • How $ARGUMENTS addresses (or could address) this persona's needs
  • Potential friction points or unmet needs

Best Practices

  • Ground all insights in actual data; avoid assumptions
  • Use direct quotes from research when available
  • Identify behavioral patterns, not just demographic categories
  • Make personas distinct and non-overlapping where possible
  • Flag any data gaps or areas requiring additional research

Further Reading

  • User Interviews: The Ultimate Guide to Research Interviews
  • Market Research: Advanced Techniques
  • Jobs-to-be-Done Masterclass with Tony Ulwick and Sabeen Sattar (video course)

Bundled with this artifact

2 files

Reference files that ship alongside this artifact. Agents pull these in only when the task needs them.

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