// AI-driven health analysis system including comprehensive analysis, risk prediction, intelligent Q&A, and report generation.
Manage allergy records including drug, food, and environmental allergies with severity tracking and medical alert integration.
Track and assess child developmental milestones based on ASQ-3 and Denver II standards. Use when user mentions development, milestones, motor skills, language, social skills, or cognitive development.
Track child illness, symptoms, fever management, and medication records. Use when user mentions child sickness, fever, cough, cold, or disease.
Child mental health screening, mood tracking, behavior assessment, anxiety and ADHD screening. Use when user mentions child emotions, behavior, attention, mood swings, or mental concerns.
Track child diet, nutrition assessment, picky eating management, and dietary advice. Use when user mentions child eating, meals, nutrition, vitamins, or feeding issues.
Child safety assessment, accident prevention, and risk evaluation for home, car, water, food, and outdoor scenarios. Use when user mentions child safety, babyproofing, or accident prevention.
| name | ai-health |
| description | AI-driven health analysis system including comprehensive analysis, risk prediction, intelligent Q&A, and report generation. |
| argument-hint | <operation_type(analysis/prediction/chat/report/status) [target] [options]> |
| allowed-tools | Read, Write |
| schema | ai-health/schema.json |
AI-driven comprehensive health analysis system providing intelligent health insights, risk prediction, and personalized recommendations.
User Input -> Parse Operation Type -> [analyze] Read Data -> Multi-dimensional Analysis -> Generate Insights -> Output Report
-> [predict] Extract Risk Factors -> Calculate Risk -> Generate Recommendations
-> [chat] Parse Query -> Retrieve Data -> Analyze -> Reply
-> [report] Generate HTML Report
-> [status] Display Configuration Status
| Input Keywords | Operation |
|---|---|
| analyze | analyze |
| predict | predict |
| chat | chat |
| report | report |
| status | status |
1. Read AI configuration and user profile
2. Read all health data sources
- Basic indicators (profile.json)
- Lifestyle data
- Mental health data
- Medical history data
3. Execute multi-dimensional analysis
- Correlation analysis (Pearson, Spearman)
- Trend analysis (linear regression, moving average)
- Anomaly detection (CUSUM, Z-score)
4. Generate personalized recommendations (Level 1-3)
5. Output text report
6. Generate HTML report (optional)
| Parameter | Description |
|---|---|
| all | All data |
| last_month | Last month |
| last_quarter | Last quarter (default) |
| last_year | Last year |
| YYYY-MM-DD | From specified date to present |
| Type | Description | Model |
|---|---|---|
| hypertension | Hypertension risk (10-year) | Framingham |
| diabetes | Diabetes risk (10-year) | ADA |
| cardiovascular | Cardiovascular risk (10-year) | Framingham |
| all | All risk predictions | Combined |
1. Read user profile and related health data
2. Extract risk factors (age, BMI, blood pressure, blood sugar, family history, etc.)
3. Apply risk prediction models
4. Calculate risk probability and grade
5. Identify modifiable risk factors
6. Generate prevention recommendations
Data Query:
What is my average sleep time?
What is my recent weight?
Trend Analysis:
How has my weight changed recently?
Has my sleep quality improved?
Correlation Query:
How does exercise affect my sleep?
Is there a relationship between diet and my weight?
Recommendation Query:
How can I improve my sleep quality?
Should I reduce my hypertension risk?
| Type | Description |
|---|---|
| comprehensive | Comprehensive health report (default) |
| quick_summary | Quick summary |
| risk_assessment | Risk assessment report |
| trend_analysis | Trend analysis report |
1. Read user data and AI configuration
2. Execute analysis based on report type
3. Call report generation script
4. Save to data/ai-reports/ directory
5. Display report file path
1. Parse operation type and parameters
2. [analyze] Load data -> Multi-dimensional analysis -> Generate insights -> Output
3. [predict] Extract risk factors -> Apply models -> Calculate risk -> Output
4. [chat] Parse query -> Retrieve data -> Analyze and reply
5. [report] Determine type -> Generate HTML -> Save
6. [status] Read configuration -> Display status
User: AI analysis
Output:
AI Health Analysis Report
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Health Index: 72/100 (Good)
Improving: Sleep quality, Exercise level
Needs Attention: BMI, Medication adherence
šÆ Risk Prediction:
Hypertension Risk: 32% (Moderate Risk)
Diabetes Risk: 18% (Low Risk)
User: AI predict hypertension risk
Output:
šÆ Hypertension Risk Prediction Report
Risk Probability: 32%
Risk Grade: š” Moderate Risk
Major Risk Factors:
1. BMI: 24.9 (Approaching overweight)
2. Systolic BP: 128 mmHg (High-normal)
User: What is my average sleep time?
Output:
Based on records from the past 90 days,
Your average sleep time is 6.8 hours.
Recommendation: Aim for 7-9 hours of sleep