スキル一覧に戻る

proto-persona

deanpeters
更新日 Yesterday
3 閲覧
4,511
575
4,511
GitHubで表示
メタgeneral

について

このスキルは、既存の調査、市場データ、チームの知見を統合し、仮説に基づいた初期ユーザーペルソナを生成します。深堀りした検証を行う前の、初期製品開発においてチームの認識合わせ、初期デザインの指針策定、知識ギャップの明確化を目的としています。十分に検証されたユーザー調査が不足している状況で、実用的な顧客プロファイルを迅速に作成する際にご利用ください。

クイックインストール

Claude Code

推奨
メイン
npx skills add deanpeters/Product-Manager-Skills -a claude-code
プラグインコマンド代替
/plugin add https://github.com/deanpeters/Product-Manager-Skills
Git クローン代替
git clone https://github.com/deanpeters/Product-Manager-Skills.git ~/.claude/skills/proto-persona

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

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.

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-PersonaValidated Persona
Created in hours/daysCreated over weeks/months
Based on assumptions + limited researchBased on extensive user research
Used to align teams earlyUsed to guide detailed design
Evolves rapidlyStable over time
Good enough to startHigh 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
  • 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

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"

Step 5: Understand Their Influences

Decision-Making Authority

Do they have the power to buy your solution?

### Attitudes & Influences

- **Decision-Making Authority:** [Yes/No + context (e.g., "Has budget authority up to $10k, needs exec approval above that")]

Quality checks:

  • 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.

Skill type: Component Suggested filename: proto-persona.md Suggested placement: /skills/components/ Dependencies: References skills/jobs-to-be-done/SKILL.md, skills/problem-statement/SKILL.md Used by: skills/positioning-statement/SKILL.md, skills/user-story/SKILL.md, skills/problem-statement/SKILL.md

GitHub リポジトリ

deanpeters/Product-Manager-Skills
パス: skills/proto-persona
0
ai-agentsai-product-managementclaude-skillspm-frameworksproduct-management

関連スキル

content-collections

メタ

このスキルは、Content Collections(Markdown/MDXファイルを型安全なデータコレクションに変換するTypeScriptファーストのツール)の本番環境でテストされた設定を提供します。Zodバリデーションによる型安全性を実現し、ブログ、ドキュメントサイト、コンテンツ重視のVite + Reactアプリケーション構築時にご利用ください。Viteプラグインの設定、MDXコンパイルから、デプロイ最適化、スキーマバリデーションまで、すべてを網羅しています。

スキルを見る

polymarket

メタ

このスキルは、開発者がPolymarket予測市場プラットフォームを活用したアプリケーション構築を可能にします。API統合による取引や市場データの取得に加え、WebSocketを介したリアルタイムデータストリーミングにより、ライブ取引や市場活動を監視できます。取引戦略の実装や、ライブ市場更新を処理するツールの作成にご利用ください。

スキルを見る

creating-opencode-plugins

メタ

このスキルは、開発者がコマンド、ファイル、LSP操作など25種類以上のイベントタイプにフックするOpenCodeプラグインを作成することを支援します。JavaScript/TypeScriptモジュール向けに、プラグイン構造、イベントAPI仕様、および実装パターンを提供します。カスタムイベント駆動ロジックでOpenCode AIアシスタントのライフサイクルをインターセプト、監視、または拡張する必要がある場合にご利用ください。

スキルを見る

sglang

メタ

SGLangは、高性能なLLMサービングフレームワークであり、RadixAttentionプレフィックスキャッシュを活用したJSON、正規表現、エージェントワークフロー向けの高速で構造化された生成を特長とします。特にプレフィックスが繰り返されるタスクにおいて、大幅に高速な推論を実現し、複雑な構造化出力やマルチターン対話に最適です。制約付きデコードが必要な場合や、広範なプレフィックス共有を伴うアプリケーションを構築する場合は、vLLMなどの代替案ではなくSGLangを選択してください。

スキルを見る