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Phase 3: UX & Intelligence Layer

Smart Module Activation (LikeInMind hooks)

Purpose

This document outlines the design, function, and implementation of Smart Module Activation, a dynamic system that leverages semantic reasoning and real-time interaction signals to surface, suggest, or activate modules within the Holonic Dashboard. Powered by LikeInMind, this system supports context-aware operations, anticipatory assistance, and intelligent responsiveness across holonic networks.

Subtitle: Context-Aware Module Triggering via Semantic Reasoning & Role-Based Cues

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1. Overview: Semantic Activation Logic

Rather than relying solely on static menus or manual navigation, Smart Module Activation enables:

Role-specific recommendations

State-aware prompts based on system dynamics (e.g., resource strain, governance backlog)

Intention-driven triggers based on user behavior, holon state, or organizational thresholds

Semantic pattern recognition to activate workflows, surface dashboards, or suggest actions

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2. Core Mechanisms

A. LikeInMind Hooks

Hooks are semantic triggers defined by combinations of:

User role + action + data state

Holon type + governance stage + system signal

Interaction pattern + metric threshold + historical context

Example Hook:

java

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IF user_role = Steward AND proposal_count > 3 AND last_vote_time > 3d

THEN suggest Governance Module → “Review Pending Proposals”

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B. Activation Types

B. Activation Types

Type

Example

🔔 Prompt Activation

“Would you like to open Governance? 3 proposals are pending.”

⚡ Auto-Surface Modules

When navigating to a stressed holon, the Resources module opens automatically.

🧭 Navigation Hints

“Your current role allows resource approval in Holon X. View now?”

🤖 Agent-Driven Initiation

Agent triggers Simulation Mode when it detects a decision conflict emerging.

There are no rows in this table

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3. Integration Touchpoints

A. Holonic Dashboard Interface

Hooks trigger:

Module highlighting

Suggestion modals

Auto-switching tabs

Notification banners

Guided walkthroughs

B. Role-Based Flow

Hook logic is scoped per-role to ensure clarity and relevance:

Members → receive proposal prompts, feedback loops

Stewards → governance backlog, automation failures

Admins → system health, coordination anomalies

Agents → execute or recommend based on logical match

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4. Hook Lifecycle Management

Hooks can be:

Predefined: Common logic across networks (e.g., voting reminders)

Custom-defined: Created by architects or admins for specific workflows

Learned: Adaptive hooks from LikeInMind based on usage patterns, NLP analysis, or semantic drift

All hooks are stored in a dynamic rulebase that evolves as:

Roles shift

Organizational complexity increases

Agentic behaviors evolve

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5. LikeInMind Semantic Engine

LikeInMind parses:

Governance grammars

Resource flow ontologies

Organizational state graphs

User interaction patterns

Signal data from Supabase + Automations

Outputs include:

Suggested module activations

Governance simulations

Escalation logic (e.g., alerting stewards)

Narrative prompts (e.g., “This holon is misaligned with its network node. Simulate realignment?”)

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6. Data Inputs & Dependencies

Smart Module Activation listens to:

Table 24

Table 24

Source

Signal Type

Supabase

Role maps, data changes, time triggers

Lovable (Frontend)

UI interaction patterns, module usage logs

n8n Automations

Process failures, delays, or anomalies

LikeInMind

Semantic intent, historical modeling, ontology tags

There are no rows in this table

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7. Governance of Activation Logic

Admins can turn on/off certain hook types

Activation logs are recorded for transparency

Users can opt-out of anticipatory prompts (via preferences)

Feedback loop: users rate activation helpfulness → improves LikeInMind learning

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8. Future Enhancements

Adaptive thresholds: thresholds for triggers adjust based on real-time context

Voice / NLP triggers: activate modules via voice commands or typed prompts

Cross-network signal sensing: detect inter-holon anomalies triggering activation

Agent awareness: LikeInMind agents self-reflect on activation accuracy over time

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Would you like a visual schema for hook flow (Trigger → Reasoning → Module Suggestion → Action)?

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