Create detailed user personas based on research and data. Develop realistic representations of target users to guide product decisions and ensure user-centered design.
npx skills add https://github.com/nicepkg/ai-workflow --skill user-persona-creation
User personas synthesize research into realistic user profiles that guide design, development, and marketing decisions.
# 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'
}
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
// 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)'
};
}
}
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"
Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Research ideation partner. Generate hypotheses, explore interdisciplinary connections, challenge assumptions, develop methodologies, identify research gaps, for creative scientific problem-solving.
Manage and trigger pre-built Zapier workflows and MCP tool orchestration. Use when user mentions workflows, Zaps, automations, daily digest, research, search, lead tracking, expenses, or asks to "run" any process. Also handles Perplexity-based research and Google Sheets data tracking.
Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
Loop 2 of the Three-Loop Integrated Development System. META-SKILL that dynamically compiles Loop 1 plans into agent+skill execution graphs. Queen Coordinator selects optimal agents from 86-agent registry and assigns skills (when available) or custom instructions. 9-step swarm with theater detection and reality validation. Receives plans from research-driven-planning, feeds to cicd-intelligent-recovery. Use for adaptive, theater-free implementation.
Loop 1 of the Three-Loop Integrated Development System. Research-driven requirements analysis with iterative risk mitigation through 5x pre-mortem cycles using multi-agent consensus. Feeds validated, risk-mitigated plans to parallel-swarm-implementation. Use when starting new features or projects requiring comprehensive planning with <3% failure confidence and evidence-based technology selection.
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