About agent-memory-systems
agent-memory-systems is a skill for designing memory systems for agents, aimed at developers building agents with long-running interactions. It covers memory types, vector-store selection, and chunking strategies, with an emphasis on retrieval rather than storage alone.
Key features
- 01Memory architecture for different information types
- 02Vector database selection patterns
- 03Chunking strategies for retrieval
- 04Guidance on metadata filtering and temporal scoring
Use cases
- 01Designing long-term memory for agents
- 02Selecting a vector database for a use case
- 03Improving retrieval from document memory
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/agent-memory-systemsin Claude Code to confirm it loaded.
Details
From the project READMEAgent 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