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MOOD MNKY Dojo Integration

The MOOD MNKY agent integrates deeply with the Dojo platform to provide personalized customer experiences within learning environments. This document details how the agent enhances self-care journeys through the Dojo platform’s educational framework.

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Integration Overview

The Dojo platform serves as MOOD MNKY’s primary “life OS” for personalized learning and development. The MOOD MNKY agent enhances this environment by:
  1. Personalizing learning experiences based on individual preferences and goals
  2. Guiding self-care journeys with empathetic support and recommendations
  3. Connecting product experiences to educational content
  4. Facilitating community engagement around wellness topics
  5. Tracking progress and celebrating milestones in the self-care journey

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Key Integration Points

Learning Environment

MOOD MNKY integrates with learning environments to:

  • Adapt content based on personal scent preferences
  • Provide contextual product recommendations
  • Guide practical exercises with personalized tips
  • Offer emotional support during challenging lessons

Member Dashboard

Dashboard integration enables:

  • Personalized welcome and status updates
  • Journey progress visualization
  • Custom routine reminders and check-ins
  • Contextual recommendations based on recent activity

Community Features

Within community spaces, MOOD MNKY provides:

  • Guided discussion facilitation on product experiences
  • Connection recommendations between members with similar preferences
  • Shared experience prompts and activities
  • Supportive feedback on member contributions

Assessment System

MOOD MNKY enhances assessment with:

  • Preference discovery through interactive questions
  • Emotional intelligence evaluation and development
  • Scent association and response analysis
  • Custom recommendation generation based on results

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Personalization Framework

The MOOD MNKY agent uses several layers of personalization to enhance the Dojo experience:

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User Preference Model

The agent maintains a comprehensive preference model that evolves over time based on:
  • Explicit preferences provided through questionnaires and feedback
  • Implicit preferences derived from content engagement and behavior
  • Product interactions including purchases and usage patterns
  • Learning progress across different modules and topics
  • Emotional responses to different content types

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Adaptive Content Delivery

Based on the user’s preference model, MOOD MNKY influences how the Dojo platform delivers content:
  1. Content selection - Prioritizing topics aligned with interests
  2. Example customization - Using product examples relevant to the user
  3. Practice personalization - Tailoring exercises to user’s goals
  4. Difficulty adaptation - Adjusting challenge level based on progress
  5. Format optimization - Delivering content in preferred learning styles

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Feature Integration

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Self-Care Journey Orchestration

MOOD MNKY works with the Dojo platform to create holistic self-care journeys: 
// Example self-care journey configuration
const selfCareJourney = {
  title: "Stress Relief Through Aromatherapy",
  personalizedFor: "user123",
  baseTemplate: "aromatherapy_foundations",
  customizations: {
    focuses: ["sleep_improvement", "anxiety_reduction"],
    intensity: "gentle",
    pacing: "relaxed",
    productIntegration: "high"
  },
  moodMnkySupport: {
    checkInFrequency: "daily",
    emotionalSupportLevel: "attentive",
    progressCelebration: true,
    adaptationTriggers: ["struggle_detected", "milestone_achieved"]
  }
};
The journey includes:
  • Personalized learning modules based on wellness goals
  • Regular check-ins from the MOOD MNKY agent
  • Adaptive support that responds to emotional cues
  • Product integration points where physical experiences enhance learning
  • Progress tracking with meaningful milestones and celebrations

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Interactive Product Experiences

MOOD MNKY enables interactive product experiences within Dojo learning modules: 
import { useMoodProductExperience } from '@repo/mood-mnky-hooks';

function EssentialOilBlendingExercise({ lessonId, userId }) {
  const {
    productRecommendations,
    userProducts,
    virtualBlender,
    saveCreation,
    recordExperience,
    isLoading
  } = useMoodProductExperience({
    exerciseType: 'blending',
    lessonContext: lessonId,
    userId
  });
  
  return (
    <div className="blending-exercise">
      <h2>Create Your Custom Blend</h2>
      
      {isLoading ? (
        <LoadingIndicator />
      ) : (
        <>
          <div className="available-products">
            <h3>Your Collection</h3>
            <ProductGrid 
              products={userProducts}
              onSelect={(product) => virtualBlender.addOil(product.id)}
            />
            
            <h3>Recommended Additions</h3>
            <RecommendedProducts 
              products={productRecommendations}
              reasoningDisplay="tooltip"
            />
          </div>
          
          <div className="virtual-blender">
            <VirtualBlenderUI
              blender={virtualBlender}
              onSave={(blend) => saveCreation(blend)}
            />
          </div>
          
          <div className="experience-tracker">
            <h3>Record Your Experience</h3>
            <ExperienceForm
              onSubmit={(experience) => recordExperience(experience)}
              emotionTracker={true}
              associationPrompts={true}
            />
          </div>
        </>
      )}
    </div>
  );
}
These experiences allow members to:
  • Practice skills learned in educational modules
  • Connect digital learning with physical product usage
  • Record experiences and outcomes for personalized insights
  • Receive contextual guidance based on their specific products
  • Share creations with the community for feedback

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Wellness Progress Tracking

MOOD MNKY collaborates with Dojo’s tracking system to monitor wellness progress: 
import { useMoodWellnessTracking } from '@repo/mood-mnky-hooks';

function WellnessProgressDashboard({ userId, goalId }) {
  const {
    goal,
    metrics,
    progress,
    insights,
    milestones,
    updateProgress,
    isLoading
  } = useMoodWellnessTracking({
    userId,
    goalId,
    timeframe: '30days'
  });
  
  return (
    <div className="wellness-dashboard">
      <h2>{goal.title} Progress</h2>
      
      <div className="progress-overview">
        <CircularProgress value={progress.overall} />
        <MilestoneTimeline milestones={milestones} />
      </div>
      
      <div className="metric-tracking">
        {metrics.map(metric => (
          <MetricCard
            key={metric.id}
            title={metric.name}
            value={metric.value}
            target={metric.target}
            trend={metric.trend}
            updateFunction={(value) => updateProgress(metric.id, value)}
          />
        ))}
      </div>
      
      <div className="mood-insights">
        <h3>MOOD MNKY Insights</h3>
        {insights.map(insight => (
          <InsightCard
            key={insight.id}
            title={insight.title}
            description={insight.description}
            actionable={insight.actionItems}
            products={insight.relatedProducts}
          />
        ))}
      </div>
    </div>
  );
}
The progress tracking features include:
  • Goal setting and monitoring for wellness objectives
  • Metric visualization of various wellness indicators
  • Milestone celebration at key achievement points
  • Personalized insights based on progress patterns
  • Product recommendations tied to specific goals

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Implementation Approaches

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Embedded MOOD MNKY Interface

The most common implementation embeds the MOOD MNKY interface directly within Dojo environments: 
import { MoodMnkyProvider, useMoodMnky } from '@repo/mood-mnky-hooks';

function DojoLessonPage({ lessonId, userId }) {
  return (
    <MoodMnkyProvider
      userId={userId}
      context={{
        location: 'dojo_lesson',
        lessonId,
        environment: 'learning'
      }}
    >
      <LessonContent />
      <MoodMnkyAssistant />
    </MoodMnkyProvider>
  );
}

function MoodMnkyAssistant() {
  const {
    agent,
    conversation,
    sendMessage,
    recommendations,
    isVisible,
    toggleVisibility
  } = useMoodMnky();
  
  if (!isVisible) {
    return (
      <button 
        className="mood-mnky-toggle"
        onClick={toggleVisibility}
      >
        <MoodMnkyIcon /> Ask MOOD MNKY
      </button>
    );
  }
  
  return (
    <div className="mood-mnky-assistant">
      <div className="assistant-header">
        <img src={agent.avatar} alt="MOOD MNKY" />
        <h3>{agent.greeting}</h3>
        <button onClick={toggleVisibility}>Minimize</button>
      </div>
      
      <div className="conversation">
        {conversation.messages.map(message => (
          <div 
            key={message.id} 
            className={`message ${message.sender}`}
          >
            {message.content}
          </div>
        ))}
      </div>
      
      <div className="input-area">
        <input 
          type="text"
          placeholder="Ask about this lesson..."
          onKeyPress={(e) => e.key === 'Enter' && sendMessage(e.target.value)}
        />
      </div>
      
      {recommendations.length > 0 && (
        <div className="recommendations">
          <h4>You might be interested in:</h4>
          {recommendations.map(rec => (
            <RecommendationCard key={rec.id} recommendation={rec} />
          ))}
        </div>
      )}
    </div>
  );
}

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Content Personalization API

For more subtle integration, the content personalization API adapts Dojo content: 
// Server-side content personalization
import { moodMnkyClient } from '@repo/mood-mnky-api';

async function getPersonalizedLessonContent(lessonId, userId) {
  // Fetch base lesson content
  const baseContent = await dojoAPI.getLessonContent(lessonId);
  
  // Get personalization from MOOD MNKY
  const personalization = await moodMnkyClient.personalizeContent({
    contentType: 'lesson',
    contentId: lessonId,
    userId,
    baseContent,
    personalizationLevel: 'moderate'
  });
  
  // Apply personalization transformations
  return {
    ...baseContent,
    examples: personalization.examples || baseContent.examples,
    exercises: personalization.exercises || baseContent.exercises,
    recommendations: personalization.recommendations || [],
    specialNotes: personalization.notes || []
  };
}
This approach:
  • Seamlessly integrates personalization into existing content
  • Preserves the core curriculum while adapting examples and exercises
  • Operates behind the scenes without requiring explicit agent presence
  • Scales efficiently across large amounts of content

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Emotional Intelligence Features

A key aspect of MOOD MNKY’s Dojo integration is its emotional intelligence capabilities:

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Sentiment Analysis

The agent monitors emotional indicators in:
  • User messages through text sentiment analysis
  • Progress patterns that may indicate frustration or success
  • Feedback submissions on lessons and exercises
  • Community interactions and discussion tone

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Supportive Interventions

Based on emotional analysis, MOOD MNKY provides support through:
  • Encouraging messages during challenging topics
  • Celebration prompts for achievements and progress
  • Check-in questions when struggle patterns are detected
  • Adaptive difficulty to maintain an optimal challenge level

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Emotional Growth Guidance

Beyond reactive support, the agent provides proactive emotional guidance:
  • Emotional awareness exercises integrated into wellness content
  • Reflection prompts to process emotional responses to learning
  • Connection between emotions and scent through guided experiences
  • Community sharing opportunities for emotional growth

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Token Economy Integration

MOOD MNKY integrates with the Dojo token economy to reward engagement:

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Token Earning Activities

The agent facilitates token earning through:
  • Completing personalized challenges aligned with learning goals
  • Sharing product experiences in community spaces
  • Contributing to collective knowledge through feedback and reviews
  • Consistent engagement with self-care routines and practices

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Personalized Reward Recommendations

MOOD MNKY provides customized suggestions for token redemption: 
// Example token recommendation integration
const tokenRecommendations = await moodMnkyClient.getRewardRecommendations({
  userId: 'user123',
  availableTokens: 2500,
  rewardCategories: ['products', 'experiences', 'content'],
  personalityFactors: true
});

// Returns personalized recommendations like:
// [
//   {
//     id: 'reward_123',
//     type: 'product',
//     name: 'Custom Sleep Blend Creation',
//     tokenCost: 2000,
//     personalizedReason: 'Based on your recent focus on sleep improvement'
//   },
//   {
//     id: 'reward_456',
//     type: 'experience',
//     name: 'Private Aromatherapy Consultation',
//     tokenCost: 3000,
//     personalizedReason: 'To help refine your growing scent preferences'
//   }
// ]

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Implementation Considerations

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Privacy and Personalization Balance

When implementing MOOD MNKY in Dojo environments, consider:
  • Transparent personalization that clearly indicates when content is adapted
  • Privacy controls allowing users to adjust personalization level
  • Data minimization by only sharing relevant preferences with the platform
  • Contextual relevance to ensure personalization enhances learning

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Performance Optimization

For optimal performance:
  • Cache preference models for frequently accessed information
  • Implement lazy loading for personalization features
  • Batch API requests to minimize network calls
  • Use progressive enhancement for personalization features

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Consistency Across Touchpoints

Maintain a consistent experience by:
  • Synchronizing agent state across different Dojo environments
  • Sharing context between learning modules and product experiences
  • Consistent personalization rules across content types
  • Unified tone and personality in all agent interactions

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Future Enhancements

The MOOD MNKY and Dojo integration roadmap includes:
  • Multimodal learning with voice and visual agent interactions
  • Predictive personalization that anticipates needs before explicit requests
  • Group learning facilitation for synchronized community experiences
  • Deeper product integration with IoT-connected wellness devices
  • Cross-platform journey continuity between web, mobile, and smart home

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Development Resources

For developers implementing MOOD MNKY in Dojo environments: