# Serena Memory System > Interface for long-term memory and context management. Use to store and retrieve user preferences, project context, and past learnings. Use when this capability is needed. - Skill: `tomevault-io/serena-memory-system` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/serena-memory-system` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/serena-memory-system/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/serena-memory-system --- # Serena Memory System Skill This skill grants access to the `serena` MCP tools, which provide persistent memory and context awareness for the agent. ## When to use - Recalling user preferences or project history. - Storing new "memories" or learnings for future sessions. - searching for files or symbols using Serena's semantic index. ## Available Tools (Context Loaded) The following tools are available via the `serena` MCP server: ### Memory Management - `mcp__serena__list_memories`: Retrieve stored memories. - `mcp__serena__think_about_collected_information`: Process and synthesize information. - `mcp__serena__onboarding`: Access onboarding information. ### Semantic Search - `mcp__serena__find_file`: Find files semantically. - `mcp__serena__find_symbol`: Find code symbols semantically. ## Best Practices 1. **Check Memory First**: Before asking the user for repetitive info, check if it's stored in memory. 2. **Synthesize**: Use `think_about_collected_information` to consolidate complex context. ## Example Workflow 1. User: "What was the design pattern we decided on?" 2. Agent: Calls `mcp__serena__list_memories` to search for design decisions. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/verridian-ai) — claim your Tome and manage your conversions.