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.
Best use case
conversation-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using conversation-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/conversation-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How conversation-memory Compares
| Feature / Agent | conversation-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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`
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在当前目录下初始化记忆系统，生成 CLAUDE.md（可选 AGENT.md 给 Cursor 用）、MEMORY.md 和 memory/ 目录。当用户说"初始化记忆"、"搭建记忆"、"memory init"、"/memory-init"时触发。
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