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
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add davila7/claude-code-templates --skill "agent-memory-systems" -g -a claude-code -yOr manually — copy the SKILL.md below into:
~/.claude/skills/agent-memory-systems-davila7/SKILL.md---
name: agent-memory-systems
description: "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"
source: vibeship-spawner-skills (Apache 2.0)
---
# 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`
Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).