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memory-query

Query and manage AI memories using MCP tools like recall_memories and store_memory.

1|Updated Feb 23, 2026
Search Tags:#recall#ai#mcp#memory#query#management
One-click install
npx skills add https://github.com/baladithyab/engram --skill memory-query-baladithyab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-query
Source: https://github.com/baladithyab/engram/tree/main/skills/memory-query
Command: npx skills add https://github.com/baladithyab/engram --skill memory-query-baladithyab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users effectively search, understand, and manage the memories stored by the AI, ensuring relevant information is easily retrievable and organized.

Core Features & Use Cases

  • Memory Recall: Search across different memory scopes (session, project, user) using text queries.
  • Memory Management: Understand memory types (episodic, semantic, procedural, working) and scopes for targeted retrieval.
  • Guidance on Queries: Provides instructions on how to construct effective memory queries and utilize available MCP tools.
  • Use Case: If you need to find a specific decision made earlier in a project, you can use this skill to query project-scoped memories for keywords related to that decision.

Quick Start

Use the memory-query skill to recall memories about authentication patterns.

Frequently Asked Questions about memory-query

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

FAQPage Schema
How do I search and recall specific AI memories from previous sessions?▼

AI memory types include episodic, semantic, procedural, and working memories. Understanding these types and their scopes (session, project, user) facilitates targeted information retrieval and effective context management within your AI sessions.

What is the best way to manage AI memory scopes for a specific project?▼

Querying AI memory helps identify patterns and manage context by retrieving historically relevant data. By searching stored memories, you can reconstruct past decisions, understand user preferences, and maintain continuous context across sessions.

How do I store new information into AI memory using MCP tools?▼

To effectively query AI memory, construct targeted text queries that match the specific memory type and scope you are looking for. Using precise keywords related to past decisions or contexts ensures accurate retrieval via the `recall_memories` tool.

Can I retrieve past decisions made earlier in a project using AI memory search?▼

Yes, you can retrieve past decisions by querying project-scoped memories with keywords related to that decision. The memory search functionality allows you to find specific historical context and structured information stored during earlier sessions.

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