agent-memory-systems
Design Agent Memory Systems
Build intelligent memory architectures for AI agents that enable long-term learning and context-aware responses. This skill provides patterns for implementing short-term, long-term, and episodic memory that improves agent performance over time.
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「agent-memory-systems」を使用しています。 Design memory for a code review agent that learns from past reviews
期待される結果:
A code review agent should use episodic memory to store individual review sessions, semantic memory for learned coding patterns, and procedural memory for review workflows. Implement retrieval with temporal weighting to prioritize recent relevant reviews while avoiding stale context.
「agent-memory-systems」を使用しています。 What memory type should store user preferences?
期待される結果:
User preferences should be stored in semantic memory since they represent persistent learned knowledge about the user. Use episodic memory to track how preferences evolved over time. Apply metadata filtering to retrieve user-specific memories efficiently.
セキュリティ監査
安全This skill is a pure documentation resource about agent memory architecture. Static analysis flagged apparent security patterns (backtick syntax, cryptographic keywords), but manual review confirms these are false positives. The backticks are markdown code formatting in a skill list, not shell execution. No actual code, scripts, network calls, or cryptographic operations exist. This is safe for publication.
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作れるもの
AI Application Developer Building Conversational Agents
Design memory systems that allow chatbots to remember user preferences and conversation history across sessions, enabling more personalized and context-aware interactions.
DevOps Engineer Building Autonomous Agents
Implement long-term memory for agents that automate infrastructure tasks, allowing them to learn from previous deployments and avoid repeating mistakes.
Research Engineer Prototyping Agent Architectures
Explore different memory system designs and retrieval strategies to find optimal configurations for specific agent use cases.
これらのプロンプトを試す
Design a memory system for an AI agent that handles customer support conversations. The agent needs to remember user preferences, past issues, and resolution history. Specify the memory types, storage approach, and retrieval strategy.
Review this document chunking strategy for a knowledge base: [describe strategy]. What are the strengths and weaknesses? How would you improve retrieval accuracy?
Our agent memory system is storing every conversation indefinitely. This is causing slow retrieval and high storage costs. What memory management approach should we use instead?
Compare vector database options for a production agent system handling 10 million embeddings with sub-100ms retrieval requirements. Consider Pinecone, Weaviate, Chroma, and pgvector.
ベストプラクティス
- Always filter by metadata (time, user, topic) before semantic retrieval to reduce noise and improve relevance
- Implement memory decay or importance scoring to prevent old or irrelevant memories from polluting context
- Budget tokens strategically across memory types - prioritize recent, high-relevance memories in limited context windows
回避
- Storing everything forever without eviction policies - leads to retrieval pollution and increased costs
- Using a single memory type for all data types - different information needs different storage approaches
- Chunking documents without testing retrieval quality - chunks that look good semantically may not retrieve well