How does this improve Claude's performance?

By loading your style and preferences automatically, it reduces the 'preamble' overhead in the context window and ensures responses match your technical requirements.

Does this skill auto-learn from my feedback?

Yes, it monitors for correction patterns like 'Actually, I meant...' and automatically updates your profile to prevent repeating mistakes.

Where is my profile data stored?

Profile data is stored locally in a git-ignored file at .agent/.user-profile.json to ensure privacy and project isolation.

Does it share data between different projects?

No, profiles are project-specific and reside within the local project directory to maintain security and context relevance.

Can I reset or clear my preferences?

Yes, you can use the 'reset my profile' or 'clear my preferences' command to securely delete all saved profile data.

Navigator User Profile

Name: Navigator User Profile
Author: alekspetrov

byalekspetrov

•

115

生産性とワークフロー

Manages persistent user preferences and auto-learns from session corrections to create a tailored AI coding experience.

概要

Navigator User Profile implements bilateral modeling to bridge the Theory of Mind gap between developers and AI. By maintaining a persistent, git-ignored profile, it allows Claude to remember technical preferences, communication styles, and past corrections across multiple sessions. This skill transforms Claude from a generic assistant into a personalized partner that understands your specific coding standards, framework choices, and workflow habits without requiring constant repetition or lengthy prompt preambles.

主な機能

Bilateral modeling based on Theory of Mind research for 20%+ performance boosts
Automated learning from user corrections and conversational feedback
Persistent storage of technical, communication, and workflow preferences
Active project goal tracking to maintain session focus
Configurable confirmation thresholds and explanation styles
115 GitHub stars

ユースケース

Adjusting AI verbosity and explanation depth based on developer expertise
Enforcing consistent code styles and naming conventions across multiple project sessions
Reducing repetitive instructions by saving project-specific framework preferences

概要

主な機能

Bilateral modeling based on Theory of Mind research for 20%+ performance boosts
Automated learning from user corrections and conversational feedback
Persistent storage of technical, communication, and workflow preferences
Active project goal tracking to maintain session focus
Configurable confirmation thresholds and explanation styles
115 GitHub stars

ユースケース

Adjusting AI verbosity and explanation depth based on developer expertise
Enforcing consistent code styles and naming conventions across multiple project sessions
Reducing repetitive instructions by saving project-specific framework preferences