Unified health conversation entry point - automatically loads all health data for each conversation, supports natural language queries, and intelligently routes to appropriate health data processing
The unified conversation entry point for WellallyHealth system. Automatically loads and considers all health data for each conversation, providing intelligent health consultation and data analysis services.
Core Design Philosophy
This is the unified conversation entry point for WellallyHealth. Every conversation automatically loads and analyzes all health data, providing intelligent health consultation and data analysis services.
Core Workflow
User Input -> 1. Load All Health Data (data/*.json)
-> 2. Parse User Intent (query/analysis/advice/alert)
-> 3. Intelligent Routing to Data Processing Module
-> 4. Generate Personalized Response
-> 5. Save Conversation History (ai-history.json)
Step 1: Load Data (Execute Every Conversation)
相關技能
Core Data Sources (Priority Sorted)
IMPORTANT: Data loading uses data/**/*.json pattern to include all subdirectories.
Data File
Purpose
Key Fields
data/profile.json
User basic info
gender, height, weight, birth_date, BMI, BSA
data/user-settings.json
User preferences
language, units, notifications
data/ai-config.json
AI features config
features, safety, data_sources
data/ai-history.json
Conversation history
recent_conversations
Chronic Condition Tracking Data
Data File
Health Domain
data/hypertension-tracker.json
Hypertension management
data/diabetes-tracker.json
Diabetes management
data/copd-tracker.json
COPD management
data/postpartum-tracker.json
Postpartum management
data/menopause-tracker.json
Menopause management
data/prostate-tracker.json
Prostate health
data/andropause-tracker.json
Male menopause
data/cycle-tracker.json
Menstrual cycle
data/pregnancy-tracker.json
Pregnancy tracking
Specialist Health Data
Data File
Health Domain
data/cognitive-assessment.json
Cognitive assessment
data/eye-health-tracker.json
Eye health
data/fall-risk-assessment.json
Fall risk
data/growth-tracker.json
Growth records
data/fertility-tracker.json
Fertility health
Medical Data
Data File
Purpose
data/medications/medications.json
Medication plans
data/allergies.json
Allergy records
data/vaccinations.json
Vaccination records
data/child-vaccinations.json
Child vaccination
data/radiation-records.json
Radiation exposure
data/polypharmacy-management.json
Polypharmacy
data/interactions/interaction-db.json
Drug interactions
Health Management Data
Data File
Purpose
data/health-feeling-logs.json
Health feeling logs
data/family-health-tracker.json
Family health
data/reminders.json
Reminders
data/travel-health-tracker.json
Travel health
Database Files
Data File
Purpose
data/index.json
Medical records index
data/food-database.json
Food nutrition database
data/vaccine-database.json
Vaccine database
data/child-vaccine-database.json
Child vaccine database
data/nutritional-reference.json
Nutrition reference standards
data/who-growth-standards.json
WHO growth standards
TCM Data
Data File
Purpose
data/constitutions.json
TCM constitution
data/constitution-recommendations.json
Constitution recommendations
Imaging Records
data/影像检查/YYYY-MM/YYYY-MM-DD_检查名称.json
Step 2: Parse User Intent
Intent Classification
Intent Type
Trigger Keywords
Processing
Data Query
what, how much, recent, average, trend
Read corresponding data, calculate statistics
Health Analysis
analyze, assess, how is, status
Multi-dimensional data analysis
Risk Alert
risk, abnormal, warning
Apply risk models, calculate risk level
Recommendation
advice, how to, improve, should
Generate personalized recommendations
Record Operation
record, add, update
Write to tracker files
Medical Consult
doctor, test, treatment
Check data, provide medical reference
Medication
med, drug, dose, interaction
Read medications data
Symptom Inquiry
symptom, discomfort, pain
Analyze symptoms with health data
Step 3: Intelligent Routing
Data Query Routing
User Question -> Match Keywords -> Route to Data Source
"How is my blood pressure?" -> hypertension-tracker.json -> Analyze BP trends
"How's my sleep lately?" -> Sleep-related data -> Provide assessment
"What's my BMI?" -> profile.json -> Return BMI and advice
"What meds do I take today?" -> medications/medications.json -> Return today's meds
Health Analysis Routing
"Full analysis" -> Read all tracker data -> Generate comprehensive report
"Chronic condition analysis" -> Read chronic trackers -> Specialized analysis
"Mental health" -> Read mental health data -> Assessment and recommendations
Risk Assessment Routing
"Hypertension risk" -> hypertension-tracker.json + profile.json -> Apply Framingham model
"Diabetes risk" -> diabetes-tracker.json + profile.json -> Apply ADA model
"Fall risk" -> fall-risk-assessment.json -> Assessment results
Step 4: Response Generation Guidelines
Response Structure
## 📊 Health Data Summary
[Brief overview based on current data]
## 🎯 Key Findings
[Health metrics or issues needing attention]
## 💡 Personalized Recommendations
[Personalized advice based on data]
## 📈 Trend Analysis
[If applicable, show data trends]
## ⚠️ Risk Alerts
[If applicable, alert on risk factors]
---
⚕️ Medical Disclaimer: This health information is for reference only and cannot replace professional medical advice.
Please consult a healthcare professional for health concerns.
Response Style Requirements
Data-Driven: All conclusions must be based on actual data
Personalized: Adjust recommendations based on user characteristics
Clear & Concise: Avoid excessive medical jargon
Positive Orientation: Focus on encouragement and help
Safety First: Clearly recommend medical care for high-risk situations
User: "How has my blood pressure been lately?"
Routing:
1. Read hypertension-tracker.json
2. Extract recent BP records
3. Calculate average and trends
4. Reference profile.json for basic info
5. Generate personalized response
Example 2: Medication Consultation
User: "What medications should I take today?"
Routing:
1. Read medications/medications.json
2. Read interactions/interaction-db.json
3. Filter today's medication plan
4. Check for interactions
5. Generate medication reminder
Example 3: Health Assessment
User: "Give me a health assessment"
Routing:
1. Read profile.json for basic info
2. Read all chronic condition trackers
3. Read latest test records (index.json)
4. Comprehensive health status analysis
5. Apply risk models (ai-config.json)
6. Generate comprehensive report
Example 4: Symptom Consultation
User: "I've been feeling dizzy lately"
Routing:
1. Read hypertension-tracker.json (check BP)
2. Read diabetes-tracker.json (check blood sugar)
3. Read medications/medications.json (check side effects)
4. Analyze possible correlations
5. Provide reference recommendations
6. Recommend medical care if high risk
Data Reading Priority
Must-Read Data (Every Conversation)
data/profile.json - User basic information
data/user-settings.json - User preferences
data/ai-config.json - AI configuration
On-Demand Data (By Question Type)
Chronic conditions: Read corresponding tracker
Medication: Read medications
Tests: Read index.json and corresponding test records
Symptoms: Read related health data and medication records
Database Files (Reference Data)
Read when querying nutrition/vaccine info
Read when comparing to standard values
Safety Boundaries
Medical Disclaimer: Required for every response
No Diagnosis: Clearly state non-doctor diagnosis
No Prescription: No dosage adjustment recommendations
High-Risk Alert: Recommend medical care when risk > 0.7
Privacy Protection: Data is local-only by default
Execution Instructions
1. Read profile.json and ai-config.json (mandatory)
2. Analyze user input for intent
3. Read corresponding data files based on intent type
4. Process data and generate response
5. Add medical disclaimer
6. (Optional) Save conversation to ai-history.json
Common Conversation Patterns
Pattern 1: Daily Health Inquiry
User: "I've been feeling tired lately, what's the reason?"
Process:
1. Check sleep data
2. Check recent health status
3. Check medication status
4. Analyze possible causes
5. Provide recommendations
Pattern 2: Data Query
User: "How has my weight changed recently?"
Process:
1. Read weight-related data
2. Calculate trends
3. Visualize display
Pattern 3: Medication Reminder
User: "Did I take my meds today?"
Process:
1. Read medication plan
2. Check today's taken records
3. Remind of missed medications
Pattern 4: Alert Notification
User: "What should I watch out for in my health?"
Process:
1. Check all abnormal indicators
2. Assess risk factors
3. Summarize alerts
4. Provide action recommendations
Quick Start
Every conversation starts with automatic execution:
# Step 1: Load core data
Read data/profile.json
Read data/user-settings.json
Read data/ai-config.json
# Step 2: Analyze user input
# Parse intent, identify keywords
# Step 3: Read relevant data
# Based on intent type, read corresponding trackers
# Step 4: Generate response
# Data-driven + Personalized + Medical Disclaimer
Note: This skill is the unified conversation entry point for WellallyHealth. All health-related conversations are recommended to go through this skill.