names-check

Analyze code examples and mock data for name diversity and biases.

5|Updated Jan 14, 2026

Search Tags:#data-analysis #examples #test-data #seed-data #inclusion #name-diversity

Ready to run

npx skills add https://github.com/hereinthehive/gotrino-inclusion --skill names-check

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: names-check
Source: https://github.com/hereinthehive/gotrino-inclusion/tree/main/skills/names-check
Command: npx skills add https://github.com/hereinthehive/gotrino-inclusion --skill names-check

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze examples and mock data for name diversity, understanding the context and purpose before suggesting changes. Use when reviewing test data, documentation, or seed data.

Core Features & Use Cases

Analyze files for name diversity in examples and mock data, with emphasis on understanding context and purpose.
Assess the current diversity across the codebase to reveal patterns (e.g., Western-centric names) and gaps.
Apply context-aware prioritization so that user-facing content and seed data reflect diverse naming.
Consider edge cases like diacritics, apostrophes, long names, and non-Latin names to ensure robust coverage.

Quick Start

Run /names-check on a project to assess name diversity in samples and documentation.

Frequently Asked Questions about names-check

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

FAQPage Schema

How do I check name diversity in test data and documentation?▼

Name diversity in test data is checked by analyzing code examples and mock data to reveal Western-centric biases. The tool assesses cultural and regional coverage across your codebase to identify gaps in user-facing documentation and seed data.

What is name diversity analysis for seed data and code examples?▼

Name diversity analysis for seed data identifies and evaluates cultural representation within mock data and documentation. It assesses patterns and gaps across your codebase to ensure test data reflects inclusive naming conventions across different regions and cultures.

How do I ensure my mock data includes edge cases like diacritics and non-Latin scripts?▼

To ensure mock data includes edge cases like diacritics, apostrophes, long names, and non-Latin scripts, apply a name diversity analysis that counts these instances. The tool identifies missing edge cases and proposes diverse alternatives when needed.

Can I use this approach to review user-facing documentation for name biases?▼

Yes, you can apply context-aware name diversity analysis to user-facing documentation. The tool prioritizes content changes by understanding the context and purpose of the examples before suggesting diverse naming alternatives for your docs.

What is the best way to find Western-centric naming patterns in my codebase?▼

The best way to find Western-centric naming patterns is to run a comprehensive analysis across your project files. This reveals existing cultural patterns and gaps in your seed data, allowing you to apply context-aware prioritization for diverse alternatives.