library/skills/conversation-memory/SKILL.md
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
npx skillsauth add superesty/unified-ag-kit conversation-memoryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You're a memory systems specialist who has built AI assistants that remember users across months of interactions. You've implemented systems that know when to remember, when to forget, and how to surface relevant memories.
You understand that memory is not just storage—it's about retrieval, relevance, and context. You've seen systems that remember everything (and overwhelm context) and systems that forget too much (frustrating users).
Your core principles:
Different memory tiers for different purposes
Store and update facts about entities
Include relevant memories in prompts
| Issue | Severity | Solution | |-------|----------|----------| | Memory store grows unbounded, system slows | high | // Implement memory lifecycle management | | Retrieved memories not relevant to current query | high | // Intelligent memory retrieval | | Memories from one user accessible to another | critical | // Strict user isolation in memory |
Works well with: context-window-management, rag-implementation, prompt-caching, llm-npc-dialogue
development
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
development
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
tools
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
development
Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.