naming

Generate and rank brand name candidates using multi-model evaluation.

127|8|Updated Apr 6, 2026
One-click install
npx skills add https://github.com/hanamizuki/solopreneur --skill naming-hanamizuki
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: naming
Source: https://github.com/hanamizuki/solopreneur/tree/main/plugins/marketer/skills/naming
Command: npx skills add https://github.com/hanamizuki/solopreneur --skill naming-hanamizuki

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps teams generate strong, memorable brand names for products or companies by combining a structured briefing, multi-model candidate generation, and a two-layer evaluation rubric to deliver a verified shortlist and final winner.

Core Features & Use Cases

  • Structured briefing: captures product context, constraints, and target markets to guide name generation.
  • Multi-model generation: uses Claude plus optional Codex / Gemini priors to maximize diversity.
  • Two-layer evaluation: gate-based filtering for pronounceability and relevance, followed by a scored ranking to select finalists.
  • Real-world workflows: greenfield naming and rebrand scenarios with audit trails and artifact outputs.

Quick Start

Provide a product or brand brief and desired constraints, and let the skill generate a validated, tension-tested name shortlist.

Frequently Asked Questions about naming

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate and evaluate brand name candidates for a new product?

You can generate brand name candidates by providing a structured brief with context, constraints, and target markets. The system uses multi-model generation and a two-layer evaluation rubric to produce a verified, tension-tested shortlist and a final winner.

What is the best way to handle cross-cultural and trademark considerations during brand naming?

Handling trademark and cross-cultural considerations requires enforced guardrails during the name generation workflow. The system applies a structured briefing and evaluation rubric to identify potential tensions, ensuring the final brand identity candidates are vetted for these specific constraints.

Can I use multiple AI models to maximize diversity for brand identity generation?

Yes, you can use multiple AI models to maximize diversity in brand identity generation. The workflow leverages Claude as the primary model and optionally incorporates Codex and Gemini priors to expand the variety of generated naming candidates.

Does this approach support both greenfield naming and rebrand scenarios?

Yes, this approach supports both greenfield naming and rebrand scenarios. It handles real-world workflows by capturing specific product context and constraints, generating candidates, and producing a documented audit trail with phase-by-phase artifacts for either scenario.

How does the two-tier evaluation rubric filter go-to-market name candidates?

The two-tier evaluation rubric filters go-to-market name candidates through a gate-based layer for pronounceability and relevance, followed by a scored ranking layer. This process systematically narrows the candidate pool to select the best finalists for your brand identity.