Skill: okaris/customer-persona (version 0.1.5)

--- name: customer-persona description: "Research-backed customer persona creation with market data and avatar generation. Covers demographics, psychographics, jobs-to-be-done, journey mapping, and anti-personas. Use for: marketing strategy, product development, UX research, sales enablement, content strategy. Triggers: customer persona, buyer persona, user persona, target audience, ideal customer, customer profile, audience research, user research, icp, ideal customer profile, target market, customer avatar, audience persona" allowed-tools: Bash(infsh *) --- # Customer Persona Create data-backed customer personas with research and visuals via [inference.sh](https://inference.sh) CLI. ## Quick Start ```bash curl -fsSL https://cli.inference.sh | sh && infsh login # Research your target market infsh app run tavily/search-assistant --input '{ "query": "SaaS product manager demographics pain points 2024 survey" }' # Generate a persona avatar infsh 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 }' ``` > **Install note:** The [install script](https://cli.inference.sh) only detects your OS/architecture, downloads the matching binary from `dist.inference.sh`, and verifies its SHA-256 checksum. No elevated permissions or background processes. [Manual install & verification](https://dist.inference.sh/cli/checksums.txt) available. ## 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. ```bash # Market demographics infsh app run tavily/search-assistant --input '{ "query": "product manager salary demographics 2024 survey report" }' # Pain points and challenges infsh app run exa/search --input '{ "query": "biggest challenges facing product managers SaaS companies" }' # Tool usage patterns infsh app run tavily/search-assistant --input '{ "query": "most popular tools product managers use 2024 survey" }' # Content consumption habits infsh 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. | Field | Format | Example | |-------|--------|---------| | Age range | X-Y | 30-38 | | Income range | $X-$Y | $120,000-$160,000 | | Education | Common degrees | BS Computer Science, MBA | | Location | Region/type | Urban US, major tech hubs | | Job title | Role level | Senior PM, Product Lead | | Company size | Range | 50-500 employees | | Industry | Sector | B2B SaaS | ### Step 3: Psychographics What they think, value, and believe. | Category | Questions to Answer | |----------|-------------------| | **Values** | What matters most to them professionally? | | **Attitudes** | How do they feel about their industry's direction? | | **Motivations** | What drives them at work? | | **Personality** | Analytical vs intuitive? Leader vs collaborator? | | **Interests** | What do they read/watch/listen to professionally? | | **Lifestyle** | Work-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 Type | Description | Example | |----------|-------------|---------| | **Functional** | The task they need to accomplish | "Prioritize the product backlog based on customer impact data" | | **Emotional** | How they want to feel | "Feel confident presenting to the exec team" | | **Social** | How they want to be perceived | "Be seen as the person who makes data-driven decisions" | ### Step 7: Buying Process | Stage | Behavior | |-------|----------| | **Awareness** | Reads blog posts, sees peer recommendations on LinkedIn | | **Consideration** | Compares 3-4 tools, reads G2/Capterra reviews, asks in Slack communities | | **Decision** | Requests demo, needs IT/security approval, evaluates team pricing | | **Influencers** | Engineering lead, VP of Product, CFO (for budget) | | **Objections** | "Will my team actually adopt it?", "Does it integrate with Jira?" | | **Trigger event** | New quarter with aggressive goals, new VP demanding better reporting | ### Step 8: Generate Avatar ```bash # Match demographics: age, gender, ethnicity, professional context infsh 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. | Priority | Persona | Role | |----------|---------|------| | **Primary** | The main user and buyer | Who you optimize for | | **Secondary** | Influences the buying decision | Who you need to convince | | **Tertiary** | Uses the product occasionally | Who you support, not target | ## Validation Personas based on assumptions are fiction. Validate with: | Method | What You Learn | |--------|---------------| | Customer interviews (5-10) | Real language, real pain points | | Support ticket analysis | Actual problems, not assumed ones | | Analytics data | Actual behavior, not reported behavior | | Survey (50+ responses) | Quantified patterns across segments | | Sales call recordings | Objections, buying triggers, language | ## Common Mistakes | Mistake | Problem | Fix | |---------|---------|-----| | Based on assumptions | Fiction, not research | Start with data | | Too many personas (6+) | Can't serve everyone well | Max 3-4 | | Vague pain points | Not actionable | Quantify everything | | Demographics only | Misses motivations and behavior | Add psychographics, JTBD | | Never updated | Becomes outdated | Review quarterly | | No anti-persona | Wasted effort on wrong customers | Define who you're NOT for | | Single persona for all | Different users have different needs | Primary/secondary/tertiary | ## Related Skills ```bash 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: `infsh app list`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)