Memory Skill
Save important conversation context, decisions, and insights as persistent memory files for future reference.
Commands
memorize
Saves a memo of the current conversation to a memory file.
Usage: User says "memorize this", "save this to memory", "remember this", or similar.
Workflow:
- •
Analyze the conversation to identify:
- •Key decisions made
- •Important context or insights
- •Relevant code patterns or architecture
- •Files that were discussed or modified
- •
Generate a memory ID based on the topic (lowercase, hyphens, e.g.,
auth-flow-decision,api-refactor-plan) - •
Create the memory file at
.cursor/skills/memory/memories/<memory-id>.md - •
Confirm the memory was saved with the file path
Memory File Format
markdown
---
name: <memory-id>
created: <YYYY-MM-DD>
tags: [<relevant>, <tags>]
---
# <Title>
## Summary
<Brief 2-3 sentence summary of what this memory captures>
## Context
<Detailed description of the conversation context, decisions made, and reasoning>
## Diagram
<Include a mermaid diagram if the memory involves architecture, flow, or relationships>
```mermaid
graph TD
A[Component] --> B[Component]
```
Relevant Files
<List of files that are relevant to this memory>- •
path/to/file1.ts- <brief description> - •
path/to/file2.py- <brief description>
Notes
<Any additional notes, caveats, or follow-up items>
code
## Guidelines
- **Be concise**: Capture the essence, not every detail
- **Include diagrams**: When the memory involves architecture, flows, or relationships, add a mermaid diagram
- **List relevant files**: Always include files that were discussed or are relevant to the memory
- **Use descriptive IDs**: Memory IDs should be meaningful and searchable
- **Add tags**: Include tags for easier discovery later
## Example
If a conversation discussed implementing a caching layer for the API:
```markdown
---
name: api-caching-decision
created: 2026-01-29
tags: [api, caching, redis, performance]
---
# API Caching Strategy Decision
## Summary
Decided to implement Redis-based caching for expensive API endpoints with a 5-minute TTL for user data and 1-hour TTL for static content.
## Context
The API was experiencing performance issues with repeated database queries for the same data. After discussing options (in-memory, Redis, file-based), Redis was chosen for its persistence and ability to share cache across instances.
## Diagram
```mermaid
graph LR
Client --> API
API --> Cache{Redis}
Cache -->|miss| DB[(Database)]
Cache -->|hit| API
Relevant Files
- •
src/backend/cache.py- cache implementation - •
src/backend/config.py- cache configuration - •
src/backend/router.py- endpoints using cache
Notes
- •Consider adding cache invalidation on data updates
- •Monitor cache hit rates after deployment
code