How does this differ from a Codebase Expert?

Codebase Experts focus on domain-specific code knowledge stored in the file system, while Product Experts focus on user-specific behavioral data stored in databases (like JSONB) to drive UI personalization.

Does this skill help with data privacy?

Yes, it includes patterns for data minimization, user control (clear/disable expertise), and data retention policies to ensure personalization remains compliant and ethical.

How can I manage latency in personalized AI features?

The skill recommends strategies such as pre-computation of expertise, progressive loading (skeleton states), and tiered personalization to ensure AI thinking time doesn't degrade user experience.

What is a Product Expert in Claude Code?

A Product Expert is a specialized agent architecture designed to learn from individual user actions to provide personalized, adaptive UX and content within an application.

Product Expert Design

Name: Product Expert Design
Author: melodic-software

bymelodic-software

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데이터 과학 및 ML

Designs adaptive user-facing agent experts to create personalized product experiences and dynamic UX.

소개

This skill provides a comprehensive framework for building AI-driven product features that learn from user behavior to deliver personalized interfaces. It guides developers through the 'Act-Learn-Reuse' architecture, offering structured schemas for user expertise, strategies for minimizing latency, and best practices for data privacy and retention. Whether building recommendation systems or adaptive frontend components, this tool ensures AI agents provide high-value, user-specific expertise without compromising performance or privacy.

주요 기능

Standardized JSONB schemas for storing user behavior and preferences
1 GitHub stars
Act-Learn-Reuse architectural patterns for continuous learning cycles
Implementation checklists for database, backend, and agent integration
Latency optimization strategies including pre-computation and progressive loading
Data privacy and retention guidelines for ethical personalization

사용 사례

Creating adaptive SaaS dashboards that surface features based on user activity
Building an e-commerce recommendation engine that adapts to purchase history
Implementing personalized content delivery systems with user-specific mental models

소개

주요 기능

Standardized JSONB schemas for storing user behavior and preferences
1 GitHub stars
Act-Learn-Reuse architectural patterns for continuous learning cycles
Implementation checklists for database, backend, and agent integration
Latency optimization strategies including pre-computation and progressive loading
Data privacy and retention guidelines for ethical personalization

사용 사례

Creating adaptive SaaS dashboards that surface features based on user activity
Building an e-commerce recommendation engine that adapts to purchase history
Implementing personalized content delivery systems with user-specific mental models