Create a proto-persona from current research, market signals, and team knowledge. Use when you need a working customer profile before deeper validation.
Install
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add deanpeters/Product-Manager-Skills --skill proto-persona -a cursor -y
Download skills/proto-persona/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does proto-persona live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use proto-persona?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Create a proto-persona from current research, market signals, and team knowledge. Use when you need a working customer profile before deeper validation.
name: proto-persona
argument-hint: "[target user or segment]"
description: Create a proto-persona from current research, market signals, and team knowledge. Use when you need a working customer profile before deeper validation.
intent: >-
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.
type: component
theme: discovery-research
best_for:
"Getting a working customer profile before you can afford real research"
"Making team assumptions about users explicit and challengeable"
"Setting up a persona you intend to validate, not defend"
scenarios:
"We have no research budget yet but need a working customer profile to start"
"Everyone on the team pictures a different user and it's causing arguments"
estimated_time: "15-25 min"
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
Proto-Persona
Validated Persona
Created in hours/days
Created over weeks/months
Based on assumptions + limited research
Based on extensive user research
Used to align teams early
Used to guide detailed design
Evolves rapidly
Stable over time
Good enough to start
High confidence
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
### 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
What are their wants, needs, dreams?
### Goals
- [Goal 1: want, need, or dream]
- [Goal 2: want, need, or dream]
- [Goal 3: want, need, or dream]
Quality checks:
Short-term and long-term: Include tactical goals ("ship feature by Q2") and aspirational goals ("become VP within 3 years")
Personal and professional: "Spend more time with family" can be as relevant as "increase team productivity"
Procurement reality: If they're a user but not a buyer, note who approves the purchase
Decision Influencers
Who influences their decisions?
- **Decision Influencers:** [Who influences this person? (e.g., "Boss, peers in industry Slack channels, analyst reports")]
Quality checks:
Specific: Not just "their manager"—name the types of influences (peer recommendations, Gartner reports, Twitter threads, etc.)
Beliefs & Attitudes
What beliefs and attitudes shape their decisions?
- **Beliefs & Attitudes:** [Beliefs/attitudes that impact decisions (e.g., "Skeptical of tools that require training," "Values data-driven decision making")]
Quality checks:
Relevant to adoption: Focus on beliefs that affect whether they'd use your product
Step 6: Validate and Iterate
Share with the team: Does this persona resonate? Do they recognize this person?
Identify gaps: What don't we know? (Add "[ASSUMPTION—VALIDATE]" tags where uncertain)
Plan research: Use the proto-persona to guide who to interview next
Evolve it: As you learn, update the proto-persona (or graduate it to a validated persona)
Examples
See examples/sample.md for full proto-persona examples.
Mini example excerpt:
### Name
- Manager Mike
### Quotes
- "I spend more time in status meetings than actually building product."
Common Pitfalls
Pitfall 1: Demographics Without Behavior
Symptom: "28 years old, lives in NYC, has a dog"
Consequence: Demographics don't explain why someone would use your product.
Fix: Add behavioral context: "Works remotely, active in 5 Slack communities, values async communication tools."
Pitfall 2: Treating Proto-Persona as Fact
Symptom: "Manager Mike would never use feature X because he hates complexity"
Consequence: You're treating an assumption as validated research.
Fix: Add "[ASSUMPTION—VALIDATE]" tags and plan interviews to test hypotheses.
Pitfall 3: Creating 10 Proto-Personas
Symptom: Trying to model every possible user type upfront
Consequence: Analysis paralysis. Teams can't focus on a primary user.
Fix: Start with 1-2 proto-personas (primary + secondary). Add more as you validate and expand.
Pitfall 4: Fabricating Quotes
Symptom: Quotes that sound like marketing copy: "I love products that delight me!"
Consequence: Fake personas lead to fake empathy.
Fix: Use real quotes from interviews, support tickets, or sales calls. If you don't have quotes yet, note "[PLACEHOLDER—NEEDS RESEARCH]."
Pitfall 5: Never Validating
Symptom: Proto-persona created 6 months ago, never updated
Consequence: You're designing for a hypothesis that may be wrong.
Fix: Plan research sprints to validate key assumptions. Evolve the proto-persona as you learn. Graduate it to a validated persona when confidence is high.
References
Related Skills
skills/problem-statement/SKILL.md — Persona informs the "I am" section
skills/jobs-to-be-done/SKILL.md — JTBD informs persona pains/goals
skills/positioning-statement/SKILL.md — Persona is the "For [target]"
skills/user-story/SKILL.md — Stories use "As a [persona]"
External Frameworks
Alan Cooper, The Inmates Are Running the Asylum (1998) — Origin of persona concept
Jeff Gothelf, Lean UX (2013) — Proto-personas as hypothesis-driven research tools
Indi Young, Mental Models (2008) — Behavior-driven persona development
Dean's Work
Proto-Persona Profile Prompt (inspired by Productside Product Manager's Playbook)
Provenance
Adapted from prompts/proto-persona-profile.md in the https://github.com/deanpeters/product-manager-prompts repo.
Download skills/proto-persona/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does proto-persona live?
The publisher keeps it at https://github.com/deanpeters/Product-Manager-Skills.
Which agents can use proto-persona?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.