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.
Install
$ agentstack add skill-phuryn-pm-skills-user-personas ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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How agent discovery & health will work →About
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
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)
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: phuryn
- Source: phuryn/pm-skills
- License: MIT
- Homepage: https://www.productcompass.pm/p/pm-skills-2-red-team-ship
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.