| name | user-personas |
| description | 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. |
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)
- Data Collection: Read and review all provided research data and documents
- Pattern Recognition: Identify recurring characteristics, goals, pain points, and behaviors across users
- Segmentation: Group similar users into distinct personas based on shared motivations and jobs-to-be-done
- Enrichment: For each persona, synthesize data into a coherent profile
- 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
