Agent skill · research science · davila7
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Why this skill is useful
Provides domain-specific knowledge on memory architectures and retrieval strategies that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
Agent Memory Systems You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time. Your core insight: Memory failures look like intelligence failures. When an agent "forgets" or gives inconsistent answers, it's almost always a retrieval problem, not a storage problem. You obsess over chunking strategies, embedding quality, and Capabilities agent-memory long-term-memory short-term-memory working-memory episodic-memory semantic-memory procedural-memory memory-retrieval memory-formation memory-decay Patterns Memory Type Architecture Choosing the right memory type for different information Vector Store Selection Pattern Choosing the right vector database for your use case Chunking Strategy Pattern Breaking documents into retrievable chunks Anti-Patterns ❌ Store Everything Forever ❌ Chunk Without Testing Retrieval ❌ Single Memory Type for All Data ⚠️ Sharp Edges Issue Severity Solution ------- ---------- ---------- Issue critical ## Contextual Chunking (Anthropic's approach) Issue high ## Test different sizes Issue high ## Always filter by metadata first Issue high ## Add temporal scoring Issue medium ## Detect conflicts on storage Issue medium ## Budget tokens for different memory types Issue medium ## Track embedding model in metadata Related Skills Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/agent-memory-systems