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.

Alternative: One-command install (curl)

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:

Name	Age	Segment
Mia Nakamura	22	Routine Devotee
Tyler Kowalski	19	Skincare Skeptic
Sofia Gutierrez	26	Budget Beauty Maven
...

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

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.

	TinyTroupe	claude-persona
Setup	Python library + OpenAI API key	Claude Code skill — no extra API key needed
Interface	Write Python code (define agents, call functions, manage execution order)	Natural language commands (`/persona generate ...`, `/persona concept-test ...`)
Focus	General-purpose agent simulation	Marketing research: concept tests, messaging tests, packaging, feature bundles

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