conversation-memory
Systems for storing and retrieving facts about users and conversations. Prevents context overload while maintaining relevant history.
Install
mkdir -p .claude/skills/conversation-memory && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/886" && unzip -o skill.zip -d .claude/skills/conversation-memory && rm skill.zipInstalls to .claude/skills/conversation-memory
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Implement short-term and long-term memory
- →Store and update entity-based facts
- →Perform memory retrieval based on relevance
- →Consolidate memory across sessions
- →Include memories in prompts
How it works
It uses a tiered memory system to categorize information into short-term, long-term, and entity-based storage, ensuring relevant context is retrieved for current queries.
Inputs & outputs
When to use conversation-memory
- →Store user preferences
- →Maintain chat history
- →Track entity-based facts
- →Implement memory-aware prompts
About this skill
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:
- 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
When not to use it
- →When you need to store data without user isolation
- →When you have no need for memory retrieval
Limitations
- →Memory store can grow unbounded if not managed
- →Requires strict user isolation to prevent cross-user data leakage
How it compares
It focuses on retrieval and relevance rather than just storing all conversation history.
Compared to similar skills
conversation-memory side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| conversation-memory (this skill) | 8 | 6mo | No flags | Advanced |
| session-logs | 8 | 2mo | Review | Intermediate |
| context-management-context-restore | 10 | 4mo | Review | Advanced |
| zlibrary-to-notebooklm | 6 | 6mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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