User Persona Creation logo

User Persona Creation

CommunityPopular
MaxMiksa
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.

Overview

PublisherMaxMiksa
RepositoryAuto-Company
Skill nameuser-persona-creation
Stars
3.1K
Forks
493
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by MaxMiksa on GitHub. Read the source before you install it.

Installation

Install the User Persona Creation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/MaxMiksa/Auto-Company/tree/main/.claude/skills/user-persona-creation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/MaxMiksa/Auto-Company.git /tmp/Auto-Company
mkdir -p .claude/skills
cp -r /tmp/Auto-Company/.claude/skills/user-persona-creation .claude/skills/user-persona-creation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable User Persona Creation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads User Persona Creation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so User Persona Creation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 discussions

Frequently asked questions

What does the User Persona Creation AI 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.

Why use User Persona Creation on TypingMind?

Because you install it once and use it with any model. User Persona Creation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install User Persona Creation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MaxMiksa/Auto-Company/tree/main/.claude/skills/user-persona-creation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use User Persona Creation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with User Persona Creation?

As many as you like. As long as a model supports skills, you can use User Persona Creation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the User Persona Creation AI skill free?

Yes. It is published on GitHub by MaxMiksa under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇