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mem-query

Retrieve and synthesize information from multiple memory layers with sourced references.

1|Updated May 12, 2026
Search Tags:#retrieval#memory#context-aware#multi-layer#personal-data#source-citation#question-answering
One-click install
npx skills add https://github.com/cocyuhao/my-ai-skills-library --skill mem-query-cocyuhao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mem-query
Source: https://github.com/cocyuhao/my-ai-skills-library/tree/main/mem-query
Command: npx skills add https://github.com/cocyuhao/my-ai-skills-library --skill mem-query-cocyuhao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI memory query system retrieves and synthesizes information from multiple memory layers to provide personalized, context-aware answers.

Core Features & Use Cases

  • Memory retrieval across L1-L4 reads and aggregates relevant memories to inform responses.
  • Source-aware answering cites origins from memory files to support conclusions.
  • Context-aware calibration uses core values stored in L4 to tailor guidance.

Quick Start

Ask the AI to lookup information from your memory and return a sourced summary.

Frequently Asked Questions about mem-query

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

FAQPage Schema
How does multi-layer memory retrieval answer questions about personal history and habits?▼

Multi-layer memory retrieval scans L1-L4 memory layers and the AI_MEMORY/INDEX.md file to find matching keywords, reads relevant files, and synthesizes personalized answers based on your past history and habits.

How do I get context-aware answers with source citations from my personal data?▼

You can ask the AI to lookup information from your memory. It performs keyword searches across memory files, reads the matching content, and returns a sourced summary with citations from the original files.

Can I use memory query to get recommendations based on past memories and core values?▼

Yes, memory query retrieves historical preferences and applies context-aware calibration using L4 core values to tailor personalized guidance and recommendations based on your stored past memories.

What is the best way to search across multiple memory layers for specific personal preferences?▼

The best way to search personal preferences is querying the AI_MEMORY/INDEX.md file first to determine relevant layers, followed by targeted keyword searches across those memory layers to synthesize an accurate response.

Does memory query require any external dependencies or components to function?▼

No, memory query operates without external dependencies or components, relying solely on reading and aggregating existing memory files stored locally across the L1 to L4 layers to generate answers.

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