ai-product-patterns

Name: ai-product-patterns
Availability: InStock
Author: danfrdn

Guide AI-native product development using OpenAI philosophy and AI UX patterns.

Updated Feb 17, 2026

Search Tags:#product management #ai #evaluations #product strategy #ux design #hybrid approach

Ready to run

npx skills add https://github.com/danfrdn/antigravity-config --skill ai-product-patterns-danfrdn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-product-patterns
Source: https://github.com/danfrdn/antigravity-config/tree/main/skills/ai-product-patterns
Command: npx skills add https://github.com/danfrdn/antigravity-config --skill ai-product-patterns-danfrdn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps product managers and engineers build AI-native products by providing frameworks for integrating AI features, designing for future model improvements, and using evaluations as product specifications.

Core Features & Use Cases

AI-Native Product Design: Learn to build products assuming AI capabilities will rapidly improve.
Evals as Product Specs: Define product requirements through runnable test cases.
Hybrid Approaches: Combine AI strengths with traditional code for robust solutions.
AI UX Patterns: Implement effective user experiences like streaming and progressive disclosure.
Use Case: When designing a new AI-powered search feature, use this skill to define success criteria via evals, plan for future model enhancements, and decide which parts of the search should be AI-driven versus traditional code.

Quick Start

Use the ai-product-patterns skill to define evals for an AI recommendation feature.

Frequently Asked Questions about ai-product-patterns

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

FAQPage Schema

How do I define product specifications for AI-native features?▼

AI-native product specifications are defined through runnable test cases called evals. This approach replaces traditional requirements documents with executable success criteria that validate model behavior and track product quality.

How to design AI UX patterns for exponential model improvement?▼

Design AI UX patterns by assuming model capabilities will rapidly improve over time. Implement adaptive interfaces using streaming and progressive disclosure to handle changing AI outputs without breaking the user experience.

What is a hybrid approach in AI product strategy?▼

A hybrid approach in AI product strategy combines AI model strengths with traditional code to create robust solutions. This method determines which product components require AI inference versus deterministic logic for optimal reliability.

Can I use evaluations as product specs for AI search features?▼

Yes, you can use evaluations as product specs for AI search features by defining success criteria via evals. This allows you to measure retrieval accuracy, plan for future model enhancements, and validate performance systematically.

When should I not use a fully AI-driven approach in product management?▼

You should avoid a fully AI-driven approach when deterministic outcomes are critical. Implementing a hybrid approach that combines AI with traditional code ensures necessary reliability and prevents unpredictable model behavior in core workflows.