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