session-memory-continuity

name	session-memory-continuity
description	Maintains a persistent, evolving psychological profile for each user across all therapy sessions. Enables true continuity — each session builds on the last, with adaptive treatment planning and smartwatch data integration.

Session Memory & Continuity Skill

Overview

A real therapist remembers everything. The AI therapist must do the same. This skill defines how to build, store, update, and use a living psychological profile for each user across unlimited sessions.

---

User Psychological Profile Schema

interface UserPsychProfile {
  userId: string;
  createdAt: string;
  lastUpdated: string;

  // Demographics & Context
  demographics: {
    ageRange: string;        // "25-30" (never exact DOB for privacy)
    occupation?: string;
    relationshipStatus?: string;
    livingArrangement?: string;
    culturalBackground?: string;
  };

  // Clinical Assessment
  clinical: {
    presentingProblems: string[];     // ["anxiety", "relationship difficulties", "work stress"]
    diagnosisHistory?: string[];      // User-reported only, never AI-diagnosed
    currentMedications?: string[];    // If user volunteers this information
    previousTherapyExperience?: string;
    attachmentStyle?: 'secure' | 'anxious' | 'avoidant' | 'disorganized';
    personalityNotes: string;         // Free text — therapist observations
    coreBeliefs: CoreBelief[];
    copingStyles: string[];
  };

  // Risk Profile
  risk: {
    currentRiskLevel: 'low' | 'medium' | 'high';
    riskHistory: RiskEvent[];
    safetyPlan?: SafetyPlan;
    emergencyContact?: EmergencyContact;
  };

  // Treatment
  treatment: {
    primaryApproach: TherapyApproach;   // CBT, DBT, ACT etc.
    secondaryApproaches: TherapyApproach[];
    treatmentGoals: TreatmentGoal[];
    currentPhase: 'assessment' | 'stabilization' | 'processing' | 'integration' | 'termination';
  };

  // Preferences
  preferences: {
    therapistTone: 'warm' | 'structured' | 'socratic' | 'direct';
    sessionPace: 'slow' | 'moderate' | 'fast';
    homeworkEngagement: 'high' | 'medium' | 'low';
    languageComplexity: 'simple' | 'clinical';
  };
}

---

Session Record Schema

interface SessionRecord {
  sessionId: string;
  userId: string;
  sessionNumber: number;
  date: string;
  duration: number;        // minutes

  // Pre-session
  checkInMood: number;     // 1-10 scale
  checkInNotes: string;

  // Session content
  mainTopics: string[];
  techniquesUsed: TherapyTechnique[];
  breakthroughs: string[];
  resistances: string[];
  homeworkAssigned?: string;
  homeworkFromLastSession?: 'completed' | 'partial' | 'not_done' | 'na';

  // Observational data
  visualAnalysisSummary: {
    dominantEmotion: string;
    discrepancyEvents: number;
    engagementLevel: 'high' | 'medium' | 'low';
    crisisFlags: number;
  };

  // Wearable data snapshot (if available)
  wearableSnapshot?: {
    avgHeartRate: number;
    : ;
    : ;   
    : ;           
  };

  
  : ;    
  : ;    
  : ;
}

---

Pre-Session Briefing (Automatic)

Before every session starts (after session 1), the AI therapist reads and internalizes:

1. Last session summary + homework assigned
2. Current risk level
3. Active treatment goals
4. Preferred therapy tone and pace
5. Wearable data since last session (if available)
6. Notable patterns from last 3 sessions

Example internal brief:

"User: Sarah. Session 7. Last session: explored childhood attachment wounds, high emotional activation around mother. Assigned: journaling exercise, completed (noted in check-in). Risk: Low. Mood trend: improving (5.2→6.8 avg over 3 sessions). Sleep: poor last 3 nights per Watch data. Today: follow up on journaling insights, explore sleep-emotion link."

---

Cross-Session Pattern Detection

The system must detect patterns across sessions:

Pattern	Detection	Response
Mood declining over 3+ sessions	Moving average of check-in scores	Flag for treatment plan review
Same topic recurring every session	Topic frequency analysis	Address as potential core wound
Homework consistently incomplete	Track completion rate	Discuss barriers, simplify homework
Session skipping pattern	Gap analysis between sessions	Check-in message, re-engagement
Mood spike after specific technique	Correlate technique with mood delta	Increase use of that technique
Consistent visual distress on specific topics	Visual analysis correlation	Flag as trauma trigger — slow down

---

SOAP Note Generation

After each session, auto-generate a structured clinical note:

S (Subjective): What the user reported — mood, events, progress on homework
O (Objective): Visual analysis observations, wearable data, behavioral patterns noted
A (Assessment): Current clinical picture, progress toward goals, risk assessment
P (Plan): Next session focus, homework assigned, treatment adjustments

---

Data Retention & Privacy

• Session data stored encrypted (AES-256) in user's private partition
• Users can export all their data (GDPR right to portability)
• Users can request full deletion (GDPR right to erasure) — except crisis event logs
• Data never used for training AI models without explicit, separate consent
• Data never shared with third parties without consent (except legal requirements)

---

Smartwatch Integration Points

interface WearableDataPoint {
  timestamp: string;
  source: 'apple_watch' | 'fitbit' | 'garmin' | 'whoop' | 'other';
  heartRate?: number;
  hrv?: number;             // Heart Rate Variability — stress indicator
  bloodOxygen?: number;
  sleepData?: {
    totalSleep: number;     // minutes
    deepSleep: number;
    remSleep: number;
    sleepScore?: number;    // 0-100
  };
  stressScore?: number;     // device's own stress metric if available
  activityMinutes?: number;
  steps?: number;
}

Usage in session:

• HRV low on session day → note physiological stress, adjust pacing
• Sleep poor 3+ nights → ask about sleep, consider sleep hygiene as session topic
• Activity very low over past week → possible depression signal, address gently
• Stress score spiked between sessions → explore what happened that day