How does semantic search differ from keyword search?

Semantic search understands the meaning and context behind your query, allowing you to find relevant information even if the specific keywords don't match exactly.

What is a spatial search modality?

A spatial search allows you to query information based on its specific location path within a structured memory palace, such as finding all concepts stored in a 'Workshop' or 'Library' node.

Does this skill require the memory-palace-architect plugin?

While it can function independently for basic indexing, it is optimized to work with memory-palace-architect to leverage pre-defined spatial structures.

Can I search across multiple memory palaces at once?

Yes, Knowledge Locator is designed to build indices that map cross-references across your entire palace network, enabling global discovery of connected information.

Knowledge Locator

Name: Knowledge Locator
Author: athola

byathola

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Learning & Documentation

Indexes and retrieves information across memory palace networks using spatial, semantic, and sensory search modalities.

About

The Knowledge Locator is an advanced search and indexing skill designed to navigate complex mental models and structured knowledge bases stored within memory palaces. By building spatial indices and supporting multi-modal queries—including semantic meaning, sensory attributes, and associative chains—it allows users to pinpoint specific concepts or discover hidden connections between disparate data points. This skill is essential for developers and researchers managing large volumes of documentation or digital garden content who need fast, reliable access to their stored intelligence.

Key Features

10 GitHub stars
Cross-palace indexing to map and follow connections between different knowledge structures
Robust fuzzy matching for finding information using partial or incomplete context
Multi-modal search capabilities including spatial, semantic, sensory, and associative queries
Access pattern analysis to identify and optimize frequently retrieved information
High-performance retrieval with sub-500ms response times for cold queries

Use Cases

Discovering non-obvious relationships between stored technical insights and project sessions
Optimizing knowledge retrieval speeds for expansive digital gardens and second-brain systems
Finding specific concepts or patterns across multiple large-scale documentation palaces

About

Key Features

10 GitHub stars
Cross-palace indexing to map and follow connections between different knowledge structures
Robust fuzzy matching for finding information using partial or incomplete context
Multi-modal search capabilities including spatial, semantic, sensory, and associative queries
Access pattern analysis to identify and optimize frequently retrieved information
High-performance retrieval with sub-500ms response times for cold queries

Use Cases

Discovering non-obvious relationships between stored technical insights and project sessions
Optimizing knowledge retrieval speeds for expansive digital gardens and second-brain systems
Finding specific concepts or patterns across multiple large-scale documentation palaces