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memory-retrieval

Retrieve and rank memory snippets using semantic search and utility filtering.

2|Updated Feb 12, 2026
Search Tags:#retrieval#memory#knowledge-management#ai-agents#semantic-search#utility-filtering
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill memory-retrieval
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-retrieval
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/memory-retrieval
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill memory-retrieval

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

High-quality memory retrieval for AI agents by combining semantic search with utility-based ranking to surface relevant memories efficiently.

Core Features & Use Cases

  • Two-stage retrieval: semantic matching followed by utility filtering to improve precision.
  • Context-aware ranking: scores memories by recency, usefulness, and relevance.
  • Practical integration: designed for integration into agent workflows with clear usage patterns.

Quick Start

Instruct the agent to retrieve memory snippets related to the current topic using semantic search and utility filtering.

Frequently Asked Questions about memory-retrieval

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

FAQPage Schema
How does two-stage memory retrieval improve AI agent recall?▼

Two-stage memory retrieval improves recall by combining semantic search matching with utility-based ranking, filtering long-term memory snippets to surface context-aware results efficiently.

How do I retrieve relevant memory snippets for AI agents using semantic search?▼

Retrieve memory snippets by instructing the agent to perform semantic matching followed by utility filtering, scoring stored memories by recency, usefulness, and relevance.

Can I use utility filtering to rank long-term memory data in production environments?▼

Yes, utility filtering ranks long-term memory data in production environments, satisfying production-grade requirements through clear frontmatter metadata and integration hooks.

What is the best way to integrate memory retrieval into existing agent workflows?▼

Integrate memory retrieval into agent workflows by using provided activation keywords and integration hooks to apply context-aware ranking across stored memories.

Why does semantic search alone fail to surface highly relevant memories for AI agents?▼

Semantic search alone misses highly relevant memories because it lacks utility filtering, preventing context-aware ranking of long-term memory snippets by factors like recency or usefulness.

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