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knowledge-locator

@athola/claude-night-market
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Spatial indexing and retrieval system for finding information within memory palaces

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SKILL.md

name knowledge-locator
description Spatial indexing and retrieval system for finding information within memory palaces
category navigation
tags retrieval, search, indexing, recall, spatial-memory
dependencies memory-palace-architect
scripts palace_manager.py
usage_patterns search, cross-reference, discovery
complexity intermediate
estimated_tokens 500

Knowledge Locator

A spatial indexing and retrieval system for finding information within and across memory palaces. Enables multi-modal search using spatial, semantic, sensory, and associative queries.

What It Is

The Knowledge Locator provides efficient information retrieval across your memory palace network by:

  • Building and maintaining spatial indices for fast lookup
  • Supporting multiple search modalities (spatial, semantic, sensory)
  • Mapping cross-references between palaces
  • Tracking access patterns for optimization

Quick Start

Search Palaces

python scripts/palace_manager.py search "authentication" --type semantic

List All Palaces

python scripts/palace_manager.py list

When to Use

  • Finding specific concepts within one or more memory palaces
  • Cross-referencing information across different palaces
  • Discovering connections between stored information
  • Finding information using partial or contextual queries
  • Analyzing access patterns for palace optimization

Search Modalities

Mode Description Best For
Spatial Query by location path "Find concepts in the Workshop"
Semantic Search by meaning/keywords "Find authentication-related items"
Sensory Locate by sensory attributes "Blue-colored concepts"
Associative Follow connection chains "Related to OAuth"
Temporal Find by creation/access date "Recently accessed"

Core Workflow

  1. Build Index - Create spatial index of all palaces
  2. Optimize Search - Configure search strategies and heuristics
  3. Map Cross-References - Identify inter-palace connections
  4. Test Retrieval - Validate search accuracy and speed
  5. Analyze Patterns - Track and optimize based on usage

Target Metrics

  • Retrieval latency: ≤ 150ms cached, ≤ 500ms cold
  • Top-3 accuracy: ≥ 90% for semantic queries
  • Robustness: ≥ 80% success with incomplete queries

Detailed Resources

  • Index Structure: See modules/index-structure.md
  • Search Strategies: See modules/search-strategies.md
  • Cross-Reference Mapping: See modules/cross-references.md

Integration

Works with:

  • memory-palace-architect - Indexes palaces created by architect
  • session-palace-builder - Searches session-specific palaces
  • digital-garden-cultivator - Finds garden content and links