user-persona-creation
Create detailed user personas based on research and data. Develop realistic representations of target users to guide product decisions and ensure user-centered design.
Best use case
user-persona-creation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create detailed user personas based on research and data. Develop realistic representations of target users to guide product decisions and ensure user-centered design.
Teams using user-persona-creation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/user-persona-creation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How user-persona-creation Compares
| Feature / Agent | user-persona-creation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create detailed user personas based on research and data. Develop realistic representations of target users to guide product decisions and ensure user-centered design.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# User Persona Creation
## Overview
User personas synthesize research into realistic user profiles that guide design, development, and marketing decisions.
## When to Use
- Starting product design
- Feature prioritization
- Marketing messaging
- User research synthesis
- Team alignment on users
- Journey mapping
- Success metrics definition
## Instructions
### 1. **Research & Data Collection**
```python
# Gather data for persona development
class PersonaResearch:
def conduct_interviews(self, target_sample_size=12):
"""Interview target users"""
interview_guide = {
'demographics': [
'Age, gender, location',
'Job title, industry, company size',
'Experience level, education',
'Salary range, purchasing power'
],
'goals': [
'What are you trying to achieve?',
'What's most important to you?',
'What does success look like?'
],
'pain_points': [
'What frustrates you about current solutions?',
'What takes too long or is complicated?',
'What prevents you from achieving goals?'
],
'behaviors': [
'How do you currently solve this problem?',
'What tools do you use?',
'How do you learn about new solutions?'
],
'preferences': [
'How do you prefer to communicate?',
'What communication channels do you use?',
'When are you most responsive?'
]
}
return {
'sample_size': target_sample_size,
'interview_guide': interview_guide,
'output': 'Interview transcripts, notes, recordings'
}
def analyze_survey_data(self, survey_data):
"""Synthesize survey responses"""
return {
'demographics': self.segment_demographics(survey_data),
'pain_points': self.extract_pain_points(survey_data),
'goals': self.identify_goals(survey_data),
'needs': self.map_needs(survey_data),
'frequency_distribution': self.calculate_frequencies(survey_data)
}
def analyze_user_data(self):
"""Use product analytics data"""
return {
'feature_usage': 'Which features are most used',
'user_segments': 'Behavioral groupings',
'conversion_paths': 'How users achieve goals',
'churn_patterns': 'Why users leave',
'usage_frequency': 'Active vs inactive users'
}
def synthesize_data(self, interview_data, survey_data, usage_data):
"""Combine all data sources"""
return {
'primary_personas': self.identify_primary_personas(interview_data),
'secondary_personas': self.identify_secondary_personas(survey_data),
'persona_groups': self.cluster_similar_users(usage_data),
'confidence_level': 'Based on data sources and sample size'
}
```
### 2. **Persona Template**
```yaml
User Persona: Premium SaaS Buyer
---
## Demographics
Name: Sarah Chen
Age: 34
Location: San Francisco, CA
Job Title: VP Product Management
Company: Series B SaaS startup (50 employees)
Experience: 8 years in product management
Education: MBA from Stanford, BS in Computer Science
Income: $180K salary + 0.5% equity
---
## Professional Context
Industry: B2B SaaS (Project Management)
Company Size: 50-200 employees
Budget Authority: Can approve purchases up to $50K
Buying Process: 60% solo decisions, 40% committee
Evaluation Time: 4-6 weeks average
---
## Goals & Motivations
Primary Goals:
1. Improve team productivity by 25%
2. Reduce project delivery time by 30%
3. Increase visibility into project status
4. Improve team collaboration across remote locations
Success Definition:
- Team using tool daily
- 20% reduction in status meetings
- Faster decision-making
- Higher team satisfaction
---
## Pain Points
Current Challenges:
- Existing tool is slow and outdated
- Poor mobile experience
- Limited reporting capabilities
- Difficult to customize for company needs
- Vendor is unresponsive to feature requests
Frustrations:
- Wasting time in status update meetings
- Lack of real-time visibility into project health
- Can't easily identify bottlenecks
- Integration with other tools is difficult
---
## Behaviors & Preferences
Daily Tools:
- Slack: Constant communication
- Google Workspace: Document collaboration
- Jira: Technical work tracking
- Spreadsheets: Status reporting (workaround)
Work Patterns:
- Typically works 8am-6pm Pacific
- Checks email every 15 minutes
- In meetings 50% of day
- Works 20% of time outside office hours
Information Gathering:
- Reads G2/Capterra reviews: High trust
- Asks for peer recommendations: Very influential
- Requests demos: Hands-on evaluation
- Wants to see case studies: Similar companies
Decision Drivers:
- ROI and measurable impact: 40%
- User adoption potential: 30%
- Ease of implementation: 20%
- Price: 10%
---
## Technology Comfort
Tech Savviness: High (uses 15+ tools daily)
Mobile Usage: 40% of work on mobile
Prefers: Intuitive UI, minimal training
Adoption Speed: Fast (new tools in 1-2 weeks)
Integration Importance: Very high
---
## Customer Journey
Awareness: Product recommendations from peers
Consideration: Reviews, demos, talk to customers
Decision: Cost-benefit analysis, team input
Onboarding: Expects self-service + minimal support
Ongoing: Wants regular feature updates, responsive support
---
## Communication Preferences
Prefers: Email and Slack (avoid calls)
Response Time: 4-24 hours typical
Best Time: Tuesday-Thursday mornings
Frequency: Weekly updates during evaluation
Format: Data-driven, executive summaries preferred
---
## Key Quotes
"I need something that my team will actually use, not something
I have to force them to adopt."
"Show me the data on time savings, not just promises."
"Our tool should work as hard as we do - seamlessly across
all our devices and workflows."
---
## Persona Importance
Primary Persona: YES (key decision maker)
Frequency in User Base: 35% of customers
Influence: High (recommends to peers)
Revenue Impact: $30K ARR average
---
## Marketing & Sales Strategy
Messaging:
- Emphasize productivity gains and ROI
- Highlight ease of adoption
- Show mobile-first experience
- Demonstrate integrations
Sales Approach:
- Provide customer references (similar companies)
- Offer flexible demo (self-service + guided)
- Focus on time-to-value
- Provide ROI calculator
Success Metrics:
- 50% adoption within 2 months
- Net Promoter Score >50
- Upsell to higher tier within 6 months
```
### 3. **Multiple Personas**
```javascript
// Create persona set for comprehensive coverage
class PersonaFramework {
createPersonaSet(research_data) {
return {
primary_personas: [
{
name: 'Sarah (VP Product)',
percentage: '35%',
influence: 'High',
role: 'Decision maker'
},
{
name: 'Mike (Team Lead)',
percentage: '40%',
influence: 'High',
role: 'Daily user, key influencer'
},
{
name: 'Lisa (Admin)',
percentage: '25%',
influence: 'Medium',
role: 'Setup and management'
}
],
secondary_personas: [
{
name: 'John (Executive)',
percentage: '10%',
influence: 'Medium',
role: 'Budget approval'
}
],
anti_personas: [
{
name: 'Enterprise IT Director',
reason: 'Not target market, different needs',
avoid: 'Marketing to large enterprise buyers'
}
]
};
}
validatePersonas(personas) {
return {
coverage: personas.reduce((sum, p) => sum + p.percentage, 0),
primary_count: personas.filter(p => p.influence === 'High').length,
recommendations: [
'Personas cover 100% of target market',
'Focus on 2-3 primary personas',
'Plan for secondary use cases',
'Define clear anti-personas'
]
};
}
createPersonaMap(personas) {
return {
influence_x_axis: 'Low → High',
adoption_y_axis: 'Slow → Fast',
sarah_vp: { influence: 'High', adoption: 'Fast' },
mike_lead: { influence: 'Very High', adoption: 'Very Fast' },
lisa_admin: { influence: 'Medium', adoption: 'Medium' },
john_executive: { influence: 'Very High', adoption: 'Slow' },
strategy: 'Focus on Mike (influencer), design for Sarah (buyer), support Lisa (user)'
};
}
}
```
### 4. **Using Personas**
```yaml
Applying Personas to Product Decisions:
---
## Feature Prioritization
Feature: Offline Mobile Access
Sarah's Need: Medium (works with wifi)
Mike's Need: Very High (field work, poor connectivity)
Lisa's Need: Low (office based)
Decision: PRIORITIZE (high-value user needs it)
Feature: Advanced Reporting
Sarah's Need: Very High (executive visibility)
Mike's Need: Low (not his responsibility)
Lisa's Need: Medium (setup reporting)
Decision: PRIORITIZE (key buyer needs it)
Feature: Bulk Import
Sarah's Need: Medium (initial setup)
Mike's Need: Low (day-to-day use)
Lisa's Need: Very High (admin task)
Decision: PRIORITIZE (admin enablement)
---
## Journey Mapping
Sarah's Evaluation Journey:
1. Becomes aware (peer recommendation) → Email request
2. Reads reviews (G2, Capterra) → Schedule demo
3. Watches demo → Reviews case studies
4. Wants reference → Talks to 2 customers
5. Creates RFP → Evaluates pricing
6. Gets team input → Makes decision
→ Timeline: 6-8 weeks
Mike's Adoption Journey:
1. Learns about tool → Demo from Sarah
2. Gets access → Starts with 1 project
3. Learns through hands-on → Gradually adopts
4. Becomes power user → Recommends to others
→ Timeline: 4 weeks
---
## Marketing Message by Persona
For Sarah (VP Product):
Headline: "Increase project delivery speed by 30%"
Focus: ROI, team productivity, visibility
Channel: LinkedIn, industry publications
CTA: "See ROI calculator"
For Mike (Team Lead):
Headline: "Work faster, stress less"
Focus: Ease of use, mobile, collaboration
Channel: Twitter, Slack communities
CTA: "Try free 30-day trial"
For Lisa (Admin):
Headline: "Setup in 1 day, not 1 month"
Focus: Easy administration, integrations
Channel: Admin webinars
CTA: "Download admin guide"
```
## Best Practices
### ✅ DO
- Base personas on real research, not assumptions
- Include 2-3 primary personas
- Make personas specific and detailed
- Include direct user quotes
- Update personas based on new data
- Share personas across organization
- Use personas for all product decisions
- Include both goals and pain points
- Create personas for different user types
- Document research sources
### ❌ DON'T
- Create personas without research
- Create too many personas (>4 primary)
- Make personas too generic
- Ignore data in favor of assumptions
- Create personas, then forget them
- Use personas only for design
- Make personas unrealistically perfect
- Ignore secondary users
- Keep personas locked away
- Never update personas
## User Persona Tips
- Use real quotes from interviews
- Include both job and personal details
- Show clear motivations and pain points
- Make personas memorable and shareable
- Print and post personas in team space
- Reference personas in design discussionsRelated Skills
infographic-creation
Create beautiful infographics based on the given text content. Use this when users request creating infographics.
writing-user-stories
Use when an agent needs to write user stories for a project
user-research
User research methods, customer insight gathering, and problem validation for product discovery.
personalization-at-scale
Generate unique personalized first lines for hundreds of prospects using company news, LinkedIn activity, and mutual connections. Saves 10+ hours of manual research per campaign. Use when you need personalized outreach at volume.
buyer-persona-generator
Create detailed buyer personas and Ideal Customer Profiles (ICP) for B2B and B2C marketing. Generates comprehensive profiles with demographics, psychographics, pain points, goals, objections, and messaging strategies. Use when defining target audience, creating ICP, or developing customer profiles.
youtube-to-markdown
Use when user asks YouTube video extraction, get, fetch, transcripts, subtitles, or captions. Writes video details and transcription into structured markdown file.
youtube-seo-optimizer
Optimize YouTube videos for search and discovery. Generates SEO-optimized titles, descriptions, tags, hashtags, and chapters. Includes keyword research and competitor analysis. Use when publishing videos, improving discoverability, or optimizing existing content.
webfluence
Content web architecture framework. Use when diagnosing offer doc usage, content-to-conversion pathways, or why someone isn't getting sales despite traffic.
video-to-gif
Convert video clips to optimized GIFs with speed control, cropping, text overlays, and file size optimization. Create perfect GIFs for social media, documentation, and presentations.
video-title-optimizer
Optimize video titles for maximum click-through rate (CTR) and YouTube/TikTok SEO. Generates multiple title variations balancing curiosity, keywords, and platform best practices. Use when naming videos, improving CTR, or A/B testing titles.
video-script-writer
Write engaging video scripts for YouTube, TikTok, and other platforms. Creates complete scripts with hooks, main content, and CTAs. Supports various formats including tutorials, vlogs, reviews, explainers, and storytelling. Use when creating video scripts, writing YouTube content, or planning video structure.
video-script-collaborial
将视频脚本转换为更适合实际录制的口语化表达,去除书面化语言,增加自然感和亲和力。当用户提到"视频脚本"、"录制"、"口语化"、"自然一点"、"像说话一样"、"太书面了"时使用此技能。