pkking/user-personas
dotfiles/dot_config/skillshare/skills/user-personas/SKILL.md
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.
development
Updated Apr 27, 2026
$ install --global
skillsauthnpx skillsauth add pkking/dotfiles user-personasInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 27, 2026, 9:49 AM46.3s2 files scanned
SKILL.md
- 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
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)
Related Skills
pkking/writing-great-skills
testing
VerifiedTrustedCommunity
Reference for writing and editing skills well — the vocabulary and principles that make a skill predictable.
SKILL.mdUpdated Jul 13, 2026
pkking/webapp-testing
tools
VerifiedTrustedCommunity
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
SKILL.mdUpdated Jul 13, 2026
pkking/web-artifacts-builder
tools
VerifiedTrustedCommunity
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
SKILL.mdUpdated Jul 13, 2026
pkking/wayfinder
data-ai
VerifiedTrustedCommunity
Plan a huge chunk of work — more than one agent session can hold — as a shared map of investigation tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.
SKILL.mdUpdated Jul 13, 2026