analyze-user

Extract and maintain structured user preference profiles from cross-project artifacts.

7|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/dmlguq456/agent_setting --skill analyze-user
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyze-user
Source: https://github.com/dmlguq456/agent_setting/tree/main/adapters/claude/skills/analyze-user
Command: npx skills add https://github.com/dmlguq456/agent_setting --skill analyze-user

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the challenge of manually understanding and maintaining a user's cross-project working preferences by extracting consistent patterns from documents, code, presentations, and analysis artifacts.

Core Features & Use Cases

  • Cross-project Pattern Analysis: Discovers writing style, figure preferences, presentation habits, analysis methods, domain expertise, and coding conventions from user-provided sources.
  • Verified Profile Updates: Runs multi-stage extraction, consistency checks, adversarial review, and structured updates to durable user profile records.
  • Use Case: Analyze a collection of research papers, slides, and code repositories to create an evolving profile that helps future agents match the user's preferred workflows and conventions.

Quick Start

Use the analyze-user skill to update my coding conventions and writing preferences from the provided project folders.

Frequently Asked Questions about analyze-user

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

FAQPage Schema
How do I extract and maintain structured user preference profiles from cross-project artifacts?▼

To extract and maintain structured user preference profiles, this Skill analyzes documents, code, and presentations through multi-phase discovery, consistency checks, and validation reviews to build durable preference records.

Can I use user profiling to match future agent behavior with my coding conventions and writing preferences?▼

Yes, user profiling matches future agent behavior with your coding conventions and writing preferences by storing verified profile records that agents reference to adapt their workflows to your cross-project patterns.

What is the best way to build verified user profiles from research analysis tasks and behavioral patterns?▼

The best way to build verified user profiles from research analysis tasks is to run multi-stage extraction, adversarial review, and structured updates on behavioral patterns, ensuring reliable preference modeling through consensus analysis.

Does this cross-project pattern analysis work with both code repositories and writing analysis tasks?▼

Yes, cross-project pattern analysis works with both code repositories and writing analysis tasks by extracting domain expertise, coding conventions, and writing style from user-provided sources to create evolving preference profiles.

What are the limitations of analyzing user work patterns for personalized agent behavior?▼

A limitation of analyzing user work patterns for personalized agent behavior is that it requires multi-phase discovery and durable profile storage, meaning insufficient or inconsistent cross-project artifacts may reduce preference modeling accuracy.