claude-persona

Claude Code skill inspired by
TinyTroupe. It generates diverse
AI persona panels, runs agent-separated concept interviews, and delivers
structured research reports in one flow.

Who This Is For

• Marketers who need fast qualitative signal before paying for fieldwork
• Product managers testing concepts, messaging, packaging, or feature bundles
• Marketing data scientists, UX researchers, and strategy teams who want a reusable synthetic audience panel

Quick Start

Install

/plugin marketplace add takechanman1228/claude-persona
/plugin install claude-persona@claude-persona

Restart Claude Code after installation.

curl -fsSL https://raw.githubusercontent.com/takechanman1228/claude-persona/main/install.sh | bash

Run a Study

Step 1 — Build a persona panel

/persona generate 10 Gen Z skincare shoppers in the US

10 diverse personas spanning different skincare attitudes:

Full panel (10 personas)

Other examples: Moms with babies shopping for strollers in the US,
High income travelers choosing luxury hotels in Europe,
10 first-time meal kit subscribers in France, based on: 38% dual income couples, 27% families with young children

Step 2 — Explore motivations (optional but recommended)

/persona ask What frustrates you most about choosing skincare products?

Top themes surfaced:

• Hidden ingredient concentrations and unverifiable efficacy claims
• Greenwashing disguised as clean beauty
• Too many choices, too much marketing noise
• TikTok hype vs. real results
• Overpriced basics with a prestige tax on identical ingredients

Step 3 — Run a concept test

/persona concept-test Compare 3 skincare concepts for Gen Z.

A: Acne Control Serum — fights breakouts with clinically proven actives
B: Barrier Repair Cream — strengthens skin barrier, reduces redness
C: Glow Boosting Toner — everyday radiance, brightens skin tone

Results:

• B: Barrier Repair Cream — 4/10 (40%) first choice
• A: Acne Control Serum — 3/10 (30%) first choice
• C: Glow Boosting Toner — 3/10 (30%) first choice
• Purchase likelihood: mean 3.3/5, range 2–5

No clear majority — each concept appeals to a distinct attitudinal cluster.
Barrier repair won among ingredient-conscious personas. Glow toner pulled
trend-chasing personas. Acne control attracted problem-driven skeptics.

See the full demo with verbatims.

Demos

The repository ships four complete demos with pre-generated personas and full results.

• Demo: Gen Z Skincare Concept Test — 10 personas, 3 skincare concepts
• Demo: Running Shoes Concept Test — 15 personas, 3 shoe concepts
• Demo: France Meal Kit Concept Test — 10 personas, France market, based on existing customer data
• Demo: Japan AI Meeting Notes SaaS — 10 personas, B2B SaaS, Japan market

Installation Details

• Claude Code CLI or Desktop
• Python 3.10+
• pandas, matplotlib, and seaborn for the analysis pipeline

Documentation

• Installation
• Commands
• Troubleshooting
• Architecture

Project Structure

claude-persona/
├── SKILL.md
├── README.md
├── CHANGELOG.md
├── .claude-plugin/
├── assets/
├── docs/
├── scripts/
├── references/
├── templates/
├── demo/
│   ├── running-shoes/
│   ├── genz-skincare/
│   ├── france-mealkit/
│   └── japan-meeting-ai/
├── personas/
├── tests/
└── outputs/

How It Compares to TinyTroupe

claude-persona is inspired by TinyTroupe but takes a different approach.

If you're a Claude Code user who wants to run quick concept research without writing code or managing a separate API key, claude-persona is the faster path.

License

MIT