What is the difference between episodic and thinking memories?

Episodic memories store specific facts, decisions, and observations (the 'what'), while thinking memories store the reasoning patterns and analysis processes used to solve problems (the 'how').

Can I limit memory searches to specific files?

Yes, you can use scope filters such as 'file:path/to/file' to find memories relevant only to a particular part of your codebase.

How does this help with project context?

It prevents the AI from 'forgetting' why certain code exists or how a problem was previously solved, effectively building a searchable knowledge base of your development history.

When should I manually create a new memory?

You should create a memory whenever a non-obvious decision is made, a complex bug is fixed with a specific root cause, or when you want to establish a persistent user preference.

Local Recall Memory Assistant

Name: Local Recall Memory Assistant
Author: SyntesseraAI

bySyntesseraAI

Productivity & Workflow

Manages and retrieves project-specific historical context, decisions, and reasoning patterns within Claude Code.

About

The Local Recall skill empowers Claude to maintain a persistent memory of your project's evolution by storing and searching through episodic facts and thinking patterns. It enables the AI to recall previous architectural decisions, bug fixes, and reasoning processes, ensuring that context is never lost across sessions and reducing repetitive explanations. By leveraging specialized search tools for both factual data and abstract reasoning, this skill transforms Claude into a long-term collaborator that understands the unique history and conventions of your specific codebase.

Key Features

Domain-specific keyword filtering for high-precision context retrieval
0 GitHub stars
Persistent episodic memory for tracking architectural decisions and bug fixes
Scope-based searching for file-specific or area-specific historical context
Thinking memory search to recall specific reasoning and problem-solving patterns
Manual memory creation to capture important project milestones and conventions

Use Cases

Checking if a specific feature or edge case has been discussed or implemented previously
Retrieving the rationale behind a complex architectural decision made in previous sessions
Recalling a specific debugging strategy used to fix a recurring race condition

About

Key Features

Domain-specific keyword filtering for high-precision context retrieval
0 GitHub stars
Persistent episodic memory for tracking architectural decisions and bug fixes
Scope-based searching for file-specific or area-specific historical context
Thinking memory search to recall specific reasoning and problem-solving patterns
Manual memory creation to capture important project milestones and conventions

Use Cases

Checking if a specific feature or edge case has been discussed or implemented previously
Retrieving the rationale behind a complex architectural decision made in previous sessions
Recalling a specific debugging strategy used to fix a recurring race condition