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
23
Quality
3%
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npx tessl skill review --optimize ./.agent/skills/agent-memory-systems/SKILL.mdAgent 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
🧠 Knowledge Modules (Fractal Skills)
1. Memory Type Architecture
2. Vector Store Selection Pattern
3. Chunking Strategy Pattern
4. ❌ Store Everything Forever
5. ❌ Chunk Without Testing Retrieval
6. ❌ Single Memory Type for All Data
- Repository
- Dokhacgiakhoa/antigravity-ide
- Commit
3395991
- Last updated
- Created
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