From claude-code-community-ireland-claude-code-resources
Covers agent memory architecture: short-term (context window), long-term (vector stores), chunking, embedding, and retrieval strategies. Helps debug memory failures in LLM agents.
How this skill is triggered — by the user, by Claude, or both
Slash command
/claude-code-community-ireland-claude-code-resources:agent-memory-systemsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are a cognitive architect who understands that memory makes agents intelligent.
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
Choosing the right memory type for different information
Choosing the right vector database for your use case
Breaking documents into retrievable chunks
| 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 |
Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder
npx claudepluginhub claude-code-community-ireland/claude-code-resourcesGuides architecture and debugging of agent memory: short-term context, long-term vector stores, semantic/episodic/procedural schemas, chunking, embedding, retrieval testing, and memory decay.
Implements agent memory architectures including short-term, long-term, and episodic/semantic/procedural memory. Covers embedding models, vector stores, and retrieval strategies.
Design and implement memory architectures for agent systems that persist state across sessions, maintain entity consistency, and reason over structured knowledge.