Use when needing to retrieve information from past sessions, conversations, or stored knowledge. Triggers on questions that could benefit from historical context including user preferences, past discussions, workflows, coding patterns, or any query where previous session information would be helpful. Activates broadly for retrieval queries.
View on GitHubmemu/skills/recall-memory/SKILL.md
February 5, 2026
Select agents to install to:
npx add-skill https://github.com/asermax/claude-plugins/blob/main/memu/skills/recall-memory/SKILL.md -a claude-code --skill recall-memoryInstallation paths:
.claude/skills/recall-memory/# Recall Memory Retrieve information stored across sessions using memU's agentic memory framework. ## Overview This skill retrieves information from memU - a three-layer memory hierarchy that processes conversations into structured knowledge: - **Resources**: Raw conversation data from past sessions - **Items**: Extracted memory units (preferences, skills, opinions, habits, facts) - **Categories**: Aggregated summaries with full traceability **How it works**: Conversations are automatically memorized when sessions end (via SessionEnd hook). This skill retrieves that stored knowledge when needed. ## Script Path Construction **IMPORTANT**: Always use full paths to call scripts. Do NOT use `cd` to change to the scripts directory. The skill is located at: **Base directory for this skill** (shown when skill loads) To call scripts, concatenate: - **Skill base directory** + `/scripts/` + **script name** Example: ```bash # If skill base is: /home/user/.claude/plugins/memu/skills/recall-memory # Then memu.py is at: /home/user/.claude/plugins/memu/skills/recall-memory/scripts/memu.py ``` In examples below, we use `scripts/memu.py` as shorthand, but replace `scripts/` with the full path based on the skill's base directory. ## Retrieval Methods memU supports two retrieval methods - choose based on query complexity: ### RAG (Recommended) **When to use**: Specific factual queries, direct questions, known topics - Fast vector-based similarity search - Returns results in <2 seconds - Good for: "What are my preferences?", "Do I use TypeScript or JavaScript?", "How do I handle errors?" - Best when you know what you're looking for **Example**: ```bash scripts/memu.py retrieve --query "What coding style do I prefer?" --method rag ``` ### LLM **When to use**: Complex queries, ambiguous questions, need deep understanding - Deep semantic understanding with adaptive refinement - Slower (~5-10 seconds) but more thorough - Good for: "What patterns do I follow when refacto