Mmcp.market

proto-persona skill

by deanpeters·deanpeters/Product-Manager-Skills·7.1k stars

Create a proto-persona from current research, market signals, and team knowledge. Use when you need a working customer profile before deeper validation.

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Install the proto-persona skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p ~/.claude/skills
cp -r /tmp/Product-Manager-Skills/skills/proto-persona ~/.claude/skills/proto-persona
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Purpose

Create an initial, assumption-based persona profile that synthesizes available user research, market data, and stakeholder knowledge into a working hypothesis about your target user. Use this to align teams early in product development, guide initial design decisions, and identify gaps in understanding that require validation through research.

This is not a validated persona—it's a "proto" (prototype) persona that evolves as you learn more. Think of it as a structured placeholder that prevents design-by-committee while acknowledging you don't have all the answers yet.

Input

Works best with: The target user or segment you need a working profile for. Also useful: Whatever signal exists — support themes, sales anecdotes, analytics, prior research — plus the decision the persona will guide.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The skill asks who you think the user is and what you already know, then structures it and flags the assumptions needing validation.

Example invocation: Proto-persona for solo bookkeepers adopting our receipt-scanning app — signal: 30 support tickets and 4 sales call notes.

Key Concepts

What is a Proto-Persona?

A proto-persona is a lightweight, hypothesis-driven persona created from:

  • Existing research: User interviews, surveys, analytics (if available)
  • Market data: Industry reports, competitor analysis, demographic trends
  • Stakeholder knowledge: Sales, support, and team insights
  • Informed assumptions: Best guesses that need validation

Proto vs. Validated Persona

Why Use Proto-Personas?

  • Speed: Align teams quickly without waiting for months of research
  • Focus: Provides a shared reference point for "who we're building for"
  • Hypothesis framing: Makes assumptions explicit, which can then be validated
  • Prevents generic design: "Design for everyone" = design for no one

Anti-Patterns (What This Is NOT)

  • Not validated research: Don't treat it as fact—it's a hypothesis
  • Not a replacement for user research: Use it to guide research, not avoid it
  • Not demographic data alone: Age and location don't explain behavior
  • Not permanent: Proto-personas should evolve as you learn

When to Use This

  • Early-stage product development (before extensive user research)
  • Kicking off a new feature or pivot
  • Aligning stakeholders on target users
  • Identifying research gaps (who do we need to interview?)

When NOT to Use This

  • After you've done extensive user research (create a validated persona instead)
  • For mature products with known user segments (you should already have validated personas)
  • As a substitute for quantitative data (proto-personas inform research; research validates them)

Application

Use template.md for the full fill-in structure.

Step 1: Gather Available Context

Before creating a proto-persona, collect:

  • User research: Interview notes, survey results, support tickets
  • Analytics: Usage data, demographics, behavioral patterns
  • Market data: Industry reports, competitor user bases
  • Stakeholder insights: Sales/support/CS teams who interact with users
  • Product context: What problem are you solving? (reference skills/problem-statement/SKILL.md)

If missing context: Don't fabricate—note gaps and plan research to fill them.

Step 2: Define the Persona's Identity

Name

Give the persona an alliterative, memorable name (makes it easier to reference).

### Name
- [Alliterative name, e.g., "Manager Mike," "Startup Sarah," "Enterprise Emma"]

Quality checks:

  • Memorable: Can the team recall it easily?
  • Not generic: Avoid "User 1" or "Persona A"

Bio & Demographics

Describe who this person is in the real world.

### Bio & Demographics
- [Age range]
- [Geographic location]
- [Social status (married, single, family, etc.)]
- [Online presence (active on LinkedIn, avoids social media, etc.)]
- [Leisure activities]
- [Career status (job title, industry, seniority)]

Quality checks:

  • Behavioral, not just demographic: Don't stop at "30-40 years old, lives in SF"—add "Works remotely, active in Slack communities, juggles 3 side projects"
  • Context-relevant: Only include demographics that influence product decisions

Example:

  • "35-45 years old, lives in urban areas (NYC, SF, Austin)"
  • "Director-level at mid-sized tech companies (50-500 employees)"
  • "Active on LinkedIn and Twitter, attends 2-3 conferences per year"
  • "Married with young kids, values work-life balance"
  • "Plays rec sports on weekends, listens to business podcasts during commute"

Step 3: Capture Their Voice

Quotes

Use real or representative quotes that reveal how they think and speak.

### Quotes
- "[Quote 1 revealing what they say, feel, or think]"
- "[Quote 2 revealing frustrations or motivations]"
- "[Quote 3 revealing attitudes or beliefs]"

Quality checks:

  • Authentic: Use real quotes from interviews/support tickets if available
  • Revealing: Quotes should expose mindset, not just facts ("I need better tools" is weak; "I'm drowning in manual work and can't focus on strategy" is strong)

Example:

  • "I spend 10 hours a week in status meetings that could be emails."
  • "I'm tired of tools that promise automation but require a developer to set up."
  • "My team expects me to have answers immediately, but I'm constantly searching for data."

Step 4: Document Their Context

Pains

What problems or frustrations does this persona experience? (Reference skills/jobs-to-be-done/SKILL.md for structure.)

### Pains
- [Pain point 1 related to the problem space]
- [Pain point 2 related to the problem space]
- [Pain point 3 related to the problem space]

Quality checks:

  • Specific: "Frustrated with tools" is vague; "Spends 3 hours/week manually copying data between tools" is specific
  • Related to your product: Focus on pains your product could address

What is This Person Trying to Accomplish?

What behaviors, actions, or outcomes are they pursuing?

### What is This Person Trying to Accomplish?
- [Behavior or outcome 1]
- [Behavior or outcome 2]
- [Behavior or outcome 3]

Quality checks:

  • Observable: Can you see this behavior? ("Get promoted" is internal; "Deliver projects 2 weeks ahead of schedule" is observable)
  • Outcome-focused: Not tasks ("use dashboards") but results ("make data-driven decisions faster")

Goals

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