knowledge-locator

name: knowledge-locator description: Searches and navigates stored knowledge in memory palaces. Use when looking for previously stored information or cross-referencing concepts across palaces. alwaysApply: false category: navigation tags:

• retrieval
• search
• indexing
• recall
• spatial-memory
• pr-review dependencies:
• memory-palace-architect
• review-chamber scripts:
• palace_manager.py usage_patterns:
• search
• cross-reference
• discovery
• review-search complexity: intermediate model_hint: standard estimated_tokens: 500

Table of Contents

• What It Is
• Quick Start
• Search Palaces
• List All Palaces
• When to Use
• Search Modalities
• Core Workflow
• Target Metrics
• Detailed Resources
• PR Review Search
• Quick Commands
• Review Chamber Rooms
• Context-Aware Surfacing
• Integration

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

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

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

Exit Criteria

• scripts/palace_manager.py search "<query>" returns results within 500ms for a cold query against an indexed palace
• At least one of the five search modalities (spatial, semantic, sensory, associative, temporal) returns ranked results for a valid query against an existing palace
• Top-3 accuracy target of ≥ 90% is met for semantic queries when the queried concept is present in the indexed palace
• Incomplete or partial queries return results rather than errors, satisfying the ≥ 80% robustness target
• If no palace exists to search, user is directed to memory-palace-architect rather than returning an empty result silently