Agent skill · writing content · davila7
conversation-memory
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.
Why this skill is useful
Provides domain-specific knowledge on memory systems that enhance AI's ability to manage and retrieve user interactions effectively.
What it needs
About 1k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
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 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 Related Skills Works well with: context-window-management, rag-implementation, prompt-caching, llm-npc-dialogue
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/conversation-memory