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Recall - Semantic Memory Retrieval

Retrieves relevant past learnings from PostgreSQL using BGE embeddings.

8|1|Updated Jan 4, 2026
Search Tags:#context#recall#semantic search#retrieval#memory#learnings
One-click install
npx skills add https://github.com/scooter-lacroix/Maestro --skill recall-semantic-memory-retrieval
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Recall - Semantic Memory Retrieval
Source: https://github.com/scooter-lacroix/Maestro/tree/main/maestro/skills/context/recall
Command: npx skills add https://github.com/scooter-lacroix/Maestro --skill recall-semantic-memory-retrieval

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps you quickly retrieve valuable information and insights learned from previous AI sessions, preventing the need to re-discover or re-explain information.

Core Features & Use Cases

  • Semantic Search: Finds relevant learnings based on the meaning of your query, not just keywords.
  • Contextual Results: Returns top results with confidence scores and session context.
  • Use Case: If you previously discussed how to implement a specific API hook and are now facing a similar problem, /recall can bring back the exact patterns and solutions you found before.

Quick Start

Use the recall skill to find information about hook development patterns.

Frequently Asked Questions about Recall - Semantic Memory Retrieval

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

FAQPage Schema
How do I retrieve past learnings from AI sessions using semantic search?▼

You can retrieve past learnings by querying a semantic memory system that uses PostgreSQL and BGE embeddings, returning relevant session insights based on natural language meaning rather than exact keywords.

Can I use natural language queries to recall previous session context?▼

Yes, you can use natural language queries to recall previous session context. The system supports both exact and fuzzy matching to find past insights and solutions based on the meaning of your query.

Does semantic memory retrieval require PostgreSQL and BGE embeddings?▼

Yes, semantic memory retrieval requires PostgreSQL and BGE embeddings to store and search past learnings, enabling the system to match natural language queries with relevant past session insights.

How do I configure search modes for precise information retrieval from memory?▼

You can configure precise information retrieval by selecting vector-only or text-only search modes, adjusting configurable result counts, and utilizing confidence scores to evaluate the returned session context.

What is the best way to search past AI session insights without exact keywords?▼

The best way to search past AI session insights without exact keywords is using semantic search, which finds relevant learnings based on the meaning of your query and returns results with confidence scores.

Why use vector-only search instead of text-only search for retrieving learnings?▼

Vector-only search retrieves learnings based on semantic meaning using BGE embeddings, while text-only search relies on exact matching, making vector search better for finding conceptually related past insights.

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