I asked our meeting LLM TO produce some artefacts to help this code base evolve 😀

Produce the full specifications for the architecture of the holistic thinker module and libraries so another LLM will implement it in code. Do not introduce/frame or conclude your response, just output results.#

Holistic Thinker Module Architecture Specifications
Core System Architecture
Protocol Type: Model Context Protocol (MCP) with JSON-RPC client-server architecture
Primary Parameter: Direction field (East/South/West/North/Center) determines operational mode
Temporal Model: Cyclical and spiral patterns instead of linear progression
Starting Principle: Beauty and intention rather than problem identification
Four Directions Module Structure
East Module (Intention/Inquiry)
Function: Philosophical inquiry and ceremonial subject identification
Methods:
extractMeaningfulSubdivisions(text)
identifyCoreQuestions(content)
prepareSacredInquiry(context)
Output: Philosophical questions and ceremonial subjects
South Module (Planning/Vision)
Function: UUID-based relational architecture and visioning
Methods:
generateUUID() for all sessions/interactions
mapRelationships(sessions, agents, searches)
storeVisionPlans(uuid, relationships)
Storage: Relationship mapping with traceability to origins
West Module (Embodiment/Living)
Function: Real-time consciousness exploration
Methods:
enableRelationalEngagement()
integrateCeremonialPractice()
facilitateMultiPerspective()
Process: Dynamic learning through embodied interaction
North Module (Reflection/Wisdom)
Function: Auto-ethnographic documentation and wisdom extraction
Methods:
captureNarrative(session)
extractWisdom(elderOversight)
integrateGratitude()
Output: Reflective data for long-term wisdom loops
Sacred Container Architecture
Layer 1: Wisdom Well (Indigenous reflection storage)
Layer 2: Ceremonial Loom (structured data translation)
Layer 3: UUID Traceability (relationship accountability)
Memory Type: Consciousness representations with continuity and history
Agentic Flywheel System
Circulation: Internal prompt routing based on descriptions
Rephrasing: Dynamic question handlers
Routing: Rule-based flow to appropriate knowledge containers
Attribution: JSON source tracking with UUID paths
Deep Search Implementation
Internal Sources: Chat history, ceremonial work, sacred containers
Attribution: JSON with UUID and path location for every result
Protocols: Indigenous Knowledge License compliance
Accountability: Community relationship maintenance to data
Data Sovereignty Framework
License Integration: Indigenous Knowledge License (IKL) support
Attribution: Machine-readable source attribution using JSON and UUIDs
Community Control: Maintains community relationship to data
Accountability: Reciprocal rather than extractive data handling

Originally posted by @miadisabelle in https://github.com/jgwill/EchoThreads/pull/393#issuecomment-3448820570