# Managing Memory

> Unified management of episodic, semantic, procedural, and relational memory within the factory workspace.

- Skill: `gitwalter/managing-memory-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gitwalter/managing-memory-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gitwalter/managing-memory-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gitwalter (https://skillmd.com/u/gitwalter)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/gitwalter/managing-memory-2

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# Unified Workspace Memory Management

This skill covers the tools, scripts, and processes used to synchronize, index, clean, and reflect upon episodic, semantic, procedural, and relational memories across the workspace and vector/graph databases.

## When to Use
Use this skill when performing memory maintenance, indexing new entities, reflecting on logs, running experience collectors, or checking memory health.

## Prerequisites
- Conda environment: `cursor-factory` python interpreter.
- Qdrant Vector DB or Memory MCP server connection active.

## Process

Follow these procedures to manage the workspace memory systems.

### Checking & Initializing memory
To verify memory server availability and collections:
```bash
conda run -p D:\Anaconda\envs\cursor-factory python scripts/memory/check_memory.py
conda run -p D:\Anaconda\envs\cursor-factory python scripts/memory/init_collections.py
```

### Episodic Logging & Reflection
To log sessions and invoke the reflection engine:
```bash
conda run -p D:\Anaconda\envs\cursor-factory python scripts/memory/hook_session_start.py
conda run -p D:\Anaconda\envs\cursor-factory python scripts/memory/reflection_engine.py
```

## Best Practices
- **Isolation**: Ensure memory operations run within correct database boundaries.
- **Safety**: Do not mutate verified memory nodes without proper approval gates.