add-memory

Store user preferences and successful interaction patterns into long-term memory by providing the content, type, and optional importance. This persists data across sessions to adapt AI behavior and improve personalization.

Record error lessons by tagging memories with the 'error' type and assigning an importance level. This allows the system to retrieve past mistakes and apply lessons to prevent similar errors across future tasks.

Yes, you can tag and categorize memories by specific types including fact, preference, skill, error, and rule. You can also assign optional importance levels to prioritize which memories are retrieved for personalization.

Store critical user information using localized, secure memory storage to persist data across sessions. The system retrieves and applies this remembered data to adapt AI responses and behavior for future interactions.

No dependencies are required to store long-term memories. The skill operates independently to satisfy explicit memory typing and localized, secure storage requirements without needing external frameworks or modules.

The system relies on explicit user inputs to save memories and does not automatically infer preferences. Memory storage is localized and secure, meaning data retrieval is constrained to the specific environment where it was saved.