Does this skill require manual data entry?

While there is a brief initial onboarding, most learning happens naturally through observation and codebase analysis to minimize workflow disruption.

Can I review or change what Claude has remembered?

Absolutely. You can view your profile at any time, and the skill includes a confirmation protocol where Claude asks for permission before saving inferred preferences.

How does Claude learn about my preferences?

Claude learns through a mix of brief onboarding questions, analyzing your codebase for technical patterns, and observing explicit or implicit signals during your conversations.

Is my personal data kept private?

Yes. All learned data is stored locally by default and Claude follows strict privacy principles, never storing sensitive information like passwords or financial data.

User Context & Memory

Name: User Context & Memory
Author: designnotdrum

bydesignnotdrum

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生产力与工作流

Personalizes your AI interactions by proactively learning and remembering your preferences, technical stack, and working style.

关于

The User Context & Memory skill transforms Claude into a personalized assistant by building a persistent profile of your professional identity and technical preferences. By utilizing proactive onboarding, real-time conversational analysis, and codebase scanning, it identifies your preferred tools, expertise levels, and communication styles. This enables Claude to provide tailored suggestions and documentation that align with your unique workflow, ensuring interactions become more efficient and contextually aware over time while strictly maintaining user control and privacy.

主要功能

Automated codebase analysis to detect technical preferences and tools
Privacy-first architecture with local storage and opt-in synchronization
Confirmation protocol to ensure AI-inferred profile updates are accurate
Implicit preference detection from conversational signals and behavior
1 GitHub stars
Proactive onboarding flow to establish user identity and roles

使用场景

Maintaining project context across sessions by remembering preferred frameworks
Matching response verbosity and communication style to user patterns
Adapting technical explanations based on identified expertise levels

关于

主要功能

Automated codebase analysis to detect technical preferences and tools
Privacy-first architecture with local storage and opt-in synchronization
Confirmation protocol to ensure AI-inferred profile updates are accurate
Implicit preference detection from conversational signals and behavior
1 GitHub stars
Proactive onboarding flow to establish user identity and roles

使用场景

Maintaining project context across sessions by remembering preferred frameworks
Matching response verbosity and communication style to user patterns
Adapting technical explanations based on identified expertise levels