mcpField
C
customer-persona

customer-persona

github.com/magentoshMarketing & growth442 installsUpdated 12w ago
Repository

About customer-persona

A skill for creating research-backed customer personas, with guidance on demographics, motivations, pain points, buying behavior, and validation. It uses the inference.sh CLI for market research and avatar generation.

Key features

  1. 01Research market and customer needs
  2. 02Organize demographic and psychographic traits
  3. 03Map goals, pain points, and JTBD
  4. 04Define anti-personas and persona tiers
  5. 05Validate with interviews and data

Use cases

  1. 01Develop a data-backed target persona
  2. 02Identify customer needs and buying triggers
  3. 03Separate target customers from anti-personas

MCP support

Uses the inference.sh belt CLI to run research and avatar-generation apps; the README does not describe an MCP connection.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/magentosh/superpowers --skill customer-persona

After installing, type/customer-personain Claude Code to confirm it loaded.

Details

From the project README

Install the belt CLI skill: npx skills add belt-sh/cli

Customer Persona

Create data-backed customer personas with research and visuals via inference.sh CLI.

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Research your target market
belt app run tavily/search-assistant --input '{
  "query": "SaaS product manager demographics pain points 2024 survey"
}'

# Generate a persona avatar
belt app run falai/flux-dev-lora --input '{
  "prompt": "professional headshot photograph of a 35-year-old woman, product manager, friendly confident expression, modern office background, natural lighting, business casual attire, realistic portrait",
  "width": 1024,
  "height": 1024
}'

Persona Template

┌──────────────────────────────────────────────────────┐
│  [Avatar Photo]                                      │
│                                                      │
│  SARAH CHEN, 34                                      │
│  Product Manager at a Series B SaaS startup          │
│                                                      │
│  "I spend more time making reports than making       │
│   decisions."                                        │
│                                                      │
├──────────────────────────────────────────────────────┤
│  DEMOGRAPHICS          │  PSYCHOGRAPHICS             │
│  Age: 30-38            │  Values: efficiency, data   │
│  Income: $120-160K     │  Personality: analytical,   │
│  Education: BS/MBA     │    organized, collaborative │
│  Location: Urban US    │  Interests: productivity,   │
│  Role: Product/PM      │    leadership, AI tools     │
├──────────────────────────────────────────────────────┤
│  GOALS                 │  PAIN POINTS                │
│  • Ship features       │  • Too many meetings        │
│  faster                │  • Manual reporting (15     │
│  • Data-driven         │    hrs/week)                │
│  decisions             │  • Stakeholder alignment    │
│  • Team alignment      │    is slow                  │
│  • Career growth to    │  • Tool sprawl (8+ apps)   │
│    Director            │  • No single source of      │
│                        │    truth                    │
├──────────────────────────────────────────────────────┤
│  CHANNELS              │  BUYING TRIGGERS            │
│  • LinkedIn (daily)    │  • Peer recommendation      │
│  • Product Hunt        │  • Free trial experience    │
│  • Podcasts (commute)  │  • Integration with Jira    │
│  • Lenny's Newsletter  │  • Team plan pricing        │
│  • Twitter/X           │  • ROI calculator           │
└──────────────────────────────────────────────────────┘

Building a Persona Step-by-Step

Step 1: Research

Start with data, not assumptions.

# Market demographics
belt app run tavily/search-assistant --input '{
  "query": "product manager salary demographics 2024 survey report"
}'

# Pain points and challenges
belt app run exa/search --input '{
  "query": "biggest challenges facing product managers SaaS companies"
}'

# Tool usage patterns
belt app run tavily/search-assistant --input '{
  "query": "most popular tools product managers use 2024 survey"
}'

# Content consumption habits
belt app run exa/answer --input '{
  "question": "Where do product managers get their industry news and professional development?"
}'
Step 2: Demographics

Use ranges, not exact values. Personas represent a segment, not one person.

FieldFormatExample
Age rangeX-Y30-38
Income range$X-$Y$120,000-$160,000
EducationCommon degreesBS Computer Science, MBA
LocationRegion/typeUrban US, major tech hubs
Job titleRole levelSenior PM, Product Lead
Company sizeRange50-500 employees
IndustrySectorB2B SaaS
Step 3: Psychographics

What they think, value, and believe.

CategoryQuestions to Answer
ValuesWhat matters most to them professionally?
AttitudesHow do they feel about their industry's direction?
MotivationsWhat drives them at work?
PersonalityAnalytical vs intuitive? Leader vs collaborator?
InterestsWhat do they read/watch/listen to professionally?
LifestyleWork-life balance preference? Remote/hybrid/office?
Step 4: Goals

What they're trying to achieve (both professional and personal).

Professional:
- Ship features faster with fewer meetings
- Make data-driven decisions (not gut feelings)
- Get promoted to Director of Product within 2 years
- Build a more autonomous product team

Personal:
- Leave work by 6pm more often
- Be seen as a strategic leader, not a ticket manager
- Stay current with industry trends without information overload
Step 5: Pain Points

Quantify whenever possible. Vague pain = vague persona.

❌ "Has trouble with reporting"
✅ "Spends 15 hours per week creating manual reports for 4 different stakeholders"

❌ "Too many tools"
✅ "Uses 8 different tools daily (Jira, Slack, Notion, Figma, Analytics, Sheets, Docs, Email) with no unified view"

❌ "Meetings are a problem"
✅ "Averages 6 hours of meetings per day, leaving only 2 hours for deep work"
Step 6: Jobs-to-be-Done (JTBD)

Three types of jobs:

Job TypeDescriptionExample
FunctionalThe task they need to accomplish"Prioritize the product backlog based on customer impact data"
EmotionalHow they want to feel"Feel confident presenting to the exec team"
SocialHow they want to be perceived"Be seen as the person who makes data-driven decisions"
Step 7: Buying Process
StageBehavior
AwarenessReads blog posts, sees peer recommendations on LinkedIn
ConsiderationCompares 3-4 tools, reads G2/Capterra reviews, asks in Slack communities
DecisionRequests demo, needs IT/security approval, evaluates team pricing
InfluencersEngineering lead, VP of Product, CFO (for budget)
Objections"Will my team actually adopt it?", "Does it integrate with Jira?"
Trigger eventNew quarter with aggressive goals, new VP demanding better reporting
Step 8: Generate Avatar
# Match demographics: age, gender, ethnicity, professional context
belt app run falai/flux-dev-lora --input '{
  "prompt": "professional headshot photograph of a 34-year-old Asian American woman, product manager, warm confident smile, modern tech office background, natural lighting, wearing smart casual blouse, realistic portrait photography, sharp focus",
  "width": 1024,
  "height": 1024
}'

Avatar tips:

  • Match the age range, ethnicity representation, and professional context
  • Use "professional headshot photograph" for realistic results
  • Friendly, approachable expression (not stock-photo-stiff)
  • Background suggests their work environment
  • Business casual or industry-appropriate attire

The Anti-Persona

Equally important: who is NOT your customer.

ANTI-PERSONA: "Enterprise Earl"
- CTO at a 5,000+ person enterprise
- Needs SOC 2, HIPAA, on-premise deployment
- 18-month procurement cycles
- Wants white-glove onboarding and dedicated CSM
- WHY NOT: Our product is self-serve SaaS for SMB/mid-market.
  Enterprise needs would require 2+ years of product investment.

Anti-personas prevent wasted effort on customers you can't serve.

Multiple Personas

Most products have 2-4 personas. More than 4 = too many to serve well.

PriorityPersonaRole
PrimaryThe main user and buyerWho you optimize for
SecondaryInfluences the buying decisionWho you need to convince
TertiaryUses the product occasionallyWho you support, not target

Validation

Personas based on assumptions are fiction. Validate with:

MethodWhat You Learn
Customer interviews (5-10)Real language, real pain points
Support ticket analysisActual problems, not assumed ones
Analytics dataActual behavior, not reported behavior
Survey (50+ responses)Quantified patterns across segments
Sales call recordingsObjections, buying triggers, language

Common Mistakes

MistakeProblemFix
Based on assumptionsFiction, not researchStart with data
Too many personas (6+)Can't serve everyone wellMax 3-4
Vague pain pointsNot actionableQuantify everything
Demographics onlyMisses motivations and behaviorAdd psychographics, JTBD
Never updatedBecomes outdatedReview quarterly
No anti-personaWasted effort on wrong customersDefine who you're NOT for
Single persona for allDifferent users have different needsPrimary/secondary/tertiary

Related Skills

npx skills add inference-sh/skills@web-search
npx skills add inference-sh/skills@ai-image-generation
npx skills add inference-sh/skills@prompt-engineering

Browse all apps: belt app list

Comments

Markdown supported