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knowledge-locator

knowledge-locator

github.com/atholaProductivity343107 installsUpdated 1d ago
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About knowledge-locator

knowledge-locator is a skill for locating and connecting information across memory palaces. It combines spatial, semantic, sensory, associative, and temporal search with cross-reference mapping and usage-pattern analysis.

Key features

  1. 01Five search modalities
  2. 02Spatial indexing for fast lookup
  3. 03Cross-palace reference mapping
  4. 04Access-pattern analysis

Use cases

  1. 01Find concepts in one or more palaces
  2. 02Trace connections between stored information
  3. 03Retrieve content with partial queries
  4. 04Review access patterns for optimization

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/athola/claude-night-market --skill knowledge-locator

After installing, type/knowledge-locatorin Claude Code to confirm it loaded.

Bundled filesmodules/index-structure.mdmodules/search-strategies.md

Details

From the project README

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/memory_palace_cli.py search "authentication" --type semantic

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

List All Palaces
python scripts/memory_palace_cli.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

ModeDescriptionBest For
SpatialQuery by location path"Find concepts in the Workshop"
SemanticSearch by meaning/keywords"Find authentication-related items"
SensoryLocate by sensory attributes"Blue-colored concepts"
AssociativeFollow connection chains"Related to OAuth"
TemporalFind 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

The CLI has no room-scoped search: search runs across every palace, and --palace, --room, list-reviews and search-reviews do not exist (this file cited them until 2026-09-18). Filter by the palace name in the output, or use Skill(memory-palace:review-chamber), which owns the chamber's own retrieval.

python scripts/memory_palace_cli.py search "authentication"
Review Chamber Rooms
RoomContentExample 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:

There is no context-search command. Search for the area's own words instead:

python scripts/memory_palace_cli.py search "auth"

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/memory_palace_cli.py search "<query>" returns results, and says "No palaces indexed" rather than "No matches" when there is nothing to search 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

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