This is a multi-tier memory architecture for LLM conversations that splits storage into short-term, long-term, and entity-based layers. You'd reach for this when building chatbots or assistants that need to recall user context across sessions without bloating every prompt with irrelevant history. The skill emphasizes retrieval strategy over just dumping everything into storage, which is the right instinct. Watch out for unbounded growth and cross-user memory leaks, both flagged as high severity issues. Pairs naturally with context window management and RAG patterns. The source material is somewhat sparse on implementation details, but the conceptual framework around when to remember versus when to forget is solid.

skills.sh installs
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last updated
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Install

$ npx -y skills add davila7/claude-code-templates --skill conversation-memory --agent claude-code

Installs into .claude/skills of the current project.

Files

Conversation Memory

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:

  1. Memory types differ—short-term, lo

Capabilities

  • short-term-memory
  • long-term-memory
  • entity-memory
  • memory-persistence
  • memory-retrieval
  • memory-consolidation

Patterns

Tiered Memory System

Different memory tiers for different purposes

Entity Memory

Store and update facts about entities

Memory-Aware Prompting

Include relevant memories in prompts

Anti-Patterns

❌ Remember Everything

❌ No Memory Retrieval

❌ Single Memory Store

⚠️ Sharp Edges

Sponsors

Details

Repository
github.com/davila7/claude-code-templates
Category
AI & Agent Building
First seen
May 16, 2026

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