search-memory

Search hierarchical memory tiers and return relevance-ranked results.

3|Updated Dec 2, 2025

Search Tags:#context #recall #search #memory #similarity #retrieve

Ready to run

npx skills add https://github.com/X-McKay/kubani --skill search-memory-x-mckay

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: search-memory
Source: https://github.com/X-McKay/kubani/tree/main/kubani/skills/general/memory/search-memory
Command: npx skills add https://github.com/X-McKay/kubani --skill search-memory-x-mckay

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently finding specific information within a complex, multi-tiered memory system, ensuring that relevant context is readily available for decision-making.

Core Features & Use Cases

Hierarchical Memory Search: Queries working, episodic, and semantic memory tiers.
Ranked Results: Returns a relevance-scored list of memories.
Contextual Retrieval: Essential for providing AI agents with the necessary background information for tasks.
Use Case: When an agent encounters an error, it can use this skill to search its memory for similar past issues and their resolutions.

Quick Start

Search the memory system for information about 'nginx pod memory issues' and retrieve the top 10 results.

Frequently Asked Questions about search-memory

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

FAQPage Schema

How do I retrieve relevant context from an AI agent memory system?▼

To retrieve relevant context from an AI agent memory system, you query a hierarchical memory structure covering working, episodic, and semantic tiers to receive a ranked list of relevant memories based on similarity.

How does similarity search work across hierarchical memory tiers?▼

Similarity search across hierarchical memory tiers works by querying working, episodic, and semantic layers simultaneously, returning a relevance-scored list of memories matching the search query.

What do I need to provide to search an AI agent's episodic memory?▼

To search an AI agent's episodic memory, you need to provide a user ID and a search query to retrieve ranked memories from the hierarchical memory system.

Can I use contextual retrieval to find past error resolutions in memory?▼

Yes, you can use contextual retrieval to search memory for similar past issues and their resolutions, providing AI agents with necessary background information for troubleshooting.

What is the best way to search multi-tier memory for pattern matching?▼

The best way to search multi-tier memory for pattern matching is querying the hierarchical memory system with a specific search query to retrieve ranked, relevance-scored memories instantly.

When should I use contextual memory retrieval for AI agent operations?▼

You should use contextual memory retrieval for AI agent operations when specific background information or pattern matching is needed for decision-making and task execution.