knowledge-locator

Installation
SKILL.md

Table of Contents

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

Verification: Run python --version to verify Python environment.

List All Palaces

python scripts/palace_manager.py list

Verification: Run python --version to verify Python environment.

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

When NOT To Use

  • Creating new palace structures - use memory-palace-architect
  • Processing new external resources - use knowledge-intake
  • Creating new palace structures - use memory-palace-architect
  • Processing new external resources - use knowledge-intake

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/index-structure.md

PR Review Search

Search the review chamber within project palaces for past decisions and patterns.

Quick Commands

# Search review chamber by query
python scripts/palace_manager.py search "authentication" \
  --palace <project_id> \
  --room review-chamber

# List entries in specific room
python scripts/palace_manager.py list-reviews \
  --palace <project_id> \
  --room decisions

# Find by tags
python scripts/palace_manager.py search-reviews \
  --tags security,api \
  --since 2025-01-01

Verification: Run python --version to verify Python environment.

Review Chamber Rooms

Room Content Example Query
decisions/ Architectural choices "JWT vs sessions"
patterns/ Recurring solutions "error handling pattern"
standards/ Quality conventions "API error format"
lessons/ Post-mortems "outage learnings"

Context-Aware Surfacing

When starting work in a code area, surface relevant review knowledge:

# When in auth/ directory
python scripts/palace_manager.py context-search auth/

# Returns:
# - Past decisions about authentication
# - Known patterns in this area
# - Relevant standards to follow

Verification: Run python --version to verify Python environment.

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
  • review-chamber - Searches PR review knowledge in project palaces
Related skills
Installs
37
GitHub Stars
273
First Seen
Feb 27, 2026