Agent Memory skills for AI agents

Build the episodic memory layer that stores specific past events and surfaces them when relevant. Covers event extraction, structured vs. unstructured storage, decay strategies, and the patterns that distinguish helpful episodic memory from noisy "remember everything" systems. Use when an agent needs to remember concrete past interactions across sessions.

Build the agent's accumulated model of the user — preferences, expertise, context, recurring patterns. The "what we know about this user" layer that shapes every interaction. Distinct from episodic memory (specific events). Use when designing an agent that should adapt to individual users over time.

Design the memory architecture for a stateful agent — what's in context, what's in retrieval, what's in episodic store, what's in semantic store. Covers the boundaries and the tradeoffs that distinguish well-architected memory from naive blob retrieval. Use when an agent needs to remember things across turns or sessions.

Implement an agent memory system using a vector database with retrieval- augmented generation. Covers chunking strategies, embedding choices, hybrid retrieval, reranking, and the patterns that produce useful retrieval rather than noisy retrieval. Use when scaling agent memory beyond what fits in context.