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.
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 |
