memory-pattern-detection

To automatically detect user interaction patterns and create AI memories, you need a Skill that analyzes observation logs for recurring sequences and user overrides. This process logs recurring actions and categorizes them to generate or promote memories without explicit instruction.

Yes, you need a SQLite memory store to automate AI memory creation from user actions. The SQLite database is required to store the observations, generated memories, and adaptations analyzed during the pattern detection process.

AI adaptation works by analyzing observation logs for user overrides and recurring sequences, then upgrading existing memories to an 'applied' status. This automatic promotion streamlines AI adaptation by learning preferences directly from repeated interactions.

The best way to learn recurring user preferences without explicit instruction is using an automatic pattern detection mechanism. It scopes to plugin execution events, identifies recurring actions like email categorizations, and automatically logs them as memories for future use.

The automatic pattern learning process runs automatically after every plugin command to analyze observation logs. This consistent execution ensures that recurring sequences, categorizations, and user overrides are captured immediately for memory creation and promotion.