context-engine

To maintain session continuity for personalized AI interactions, you need a context management system that tracks user preferences and state. This Skill uses USER.md and STATE.md files to capture background, learning style, and conversation history across sessions.

User memory management in AI interactions is the process of tracking user preferences and session history to provide context-aware responses. This Skill captures user names, backgrounds, and venture context to maintain personalized continuity across multiple sessions.

To set up context management for tracking user preferences, activate the context engine to initialize the required USER.md and STATE.md files. On first interaction, it automatically captures user background, learning style preferences, and venture context to build a persistent memory profile.

AI personalization works without external dependencies by relying entirely on local USER.md and STATE.md files for context storage. This Skill operates independently to track conversation threads, manage opportunity banks, and adjust context windows based on user archetypes.

Yes, you can use context-aware greetings across multiple sessions by leveraging the context engine's return greeting feature. It references your last topic, opportunity bank, and room location from stored memory to provide a personalized greeting when you resume an interaction.

Session context needs separate memory files to distinctively isolate user identity from transient session state. USER.md stores persistent background and preferences, while STATE.md tracks active conversation threads and room locations, ensuring clean context window management across interactions.