memories-sanitize

Review and delete invalid or outdated entries from the AI's semantic memory database.

6|1|Updated Feb 6, 2026

Search Tags:#debugging #knowledge base #memory management #data cleaning #semantic memory #ai accuracy

Ready to run

npx skills add https://github.com/mahmoudimus/simba --skill memories-sanitize

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memories-sanitize
Source: https://github.com/mahmoudimus/simba/tree/main/skills/memories-sanitize
Command: npx skills add https://github.com/mahmoudimus/simba --skill memories-sanitize

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps maintain the integrity and accuracy of the AI's semantic memory by identifying and removing outdated, incorrect, or irrelevant information.

Core Features & Use Cases

Memory Review: Systematically review stored memories for validity.
Invalid Memory Identification: Detects memories that are outdated, superseded, too specific, incorrect, or misleading.
Memory Deletion: Removes identified invalid memories from the database.
Corrected Memory Storage: Optionally stores corrected versions of invalid memories.
Use Case: After a significant refactoring of a codebase, use this Skill to remove old memories about the previous architecture that are no longer relevant, preventing the AI from acting on stale information.

Quick Start

Review and delete invalid memories using the memories-sanitize skill.

Frequently Asked Questions about memories-sanitize

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I clean up outdated information from AI semantic memory?▼

To clean up outdated information from AI semantic memory, you systematically review stored memories, identify invalid or superseded entries, and delete them to ensure AI accuracy. This process removes stale data that may mislead future sessions.

Why does my AI use incorrect generalizations during debugging sessions?▼

Your AI uses incorrect generalizations during debugging because its semantic memory database likely contains invalid or misleading entries. Data cleaning processes can identify and purge these incorrect memories, optionally replacing them with corrected learnings.

What is the best way to remove stale knowledge base entries after a codebase refactoring?▼

The best way to remove stale knowledge base entries after a codebase refactoring is to review the semantic memory database and delete memories about the previous architecture. This prevents the AI from acting on outdated solutions.

Can I update incorrect AI memories instead of just deleting them?▼

Yes, you can update incorrect AI memories instead of just deleting them by using a memory sanitization process that optionally stores corrected versions of the invalid entries. This ensures memory accuracy while preserving valid learning context.

When do I need to sanitize semantic memory for feature development?▼

You need to sanitize semantic memory for feature development when outdated or too-specific information starts negatively impacting AI accuracy. Reviewing and purging misleading entries prevents the AI from relying on superseded solutions.