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ai-analyzer

Analyze multi-source health data to derive insights and risk indicators.

26|3|Updated Feb 21, 2026
Search Tags:#data-integration#local-processing#ai-analysis#health-data#health-report#risk-prediction
One-click install
npx skills add https://github.com/herove/frxiaobei-skills --skill ai-analyzer-herove
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-analyzer
Source: https://github.com/herove/frxiaobei-skills/tree/main/skills/youyou-health/references/ai-analyzer
Command: npx skills add https://github.com/herove/frxiaobei-skills --skill ai-analyzer-herove

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-driven health analytics aggregates diverse data sources to reveal actionable health insights, detect anomalies, and forecast risks, enabling proactive management.

Core Features & Use Cases

  • Intelligent health analysis: Integrates basic indicators, lifestyle, mental health, and medical history to produce comprehensive insights.
  • Risk prediction: Applies models for hypertension, diabetes, and cardiovascular risk, with tailored guidance.
  • Personalized reporting & Q&A: Generates AI health reports and supports natural language questions to explore trends.

Quick Start

Analyze my health data to generate a personalized risk report.

Frequently Asked Questions about ai-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a personalized health risk report from multi-source data?▼

This Skill analyzes multi-source health data to derive insights and risk indicators. By integrating medical histories, lifestyle, and sleep data, it applies Framingham, ADA, and ASCVD models to forecast hypertension and diabetes risks.

Can I run health data analysis locally without sending data to external servers?▼

Yes, health data analysis runs with local-only execution. Your multi-source data is processed on-device to derive insights, apply risk prediction models, and generate reports without sending personal health information to external servers.

What risk prediction models are used for cardiovascular and diabetes risk assessment?▼

Risk prediction applies Framingham, ADA, and ASCVD models. These models evaluate integrated personal health data, medical histories, and lifestyle factors to forecast hypertension, diabetes, and cardiovascular risks.

Does health risk forecasting work with lifestyle and mental health data or only medical histories?▼

Risk forecasting works with both medical histories and lifestyle data. It integrates basic indicators, sleep, nutrition, and mental health information to produce comprehensive insights and layered personalized recommendations.

How do I ask natural language questions about my health analysis results?▼

After generating your health report, you can ask natural language questions to explore trends. The analysis supports interactive Q&A to help you understand risk indicators and personalized guidance derived from your integrated data.

What format does the AI health report output use for sharing results?▼

The AI health report is generated in HTML format. This allows the comprehensive report containing risk indicators, layered recommendations, and personalized guidance to be easily viewed and shared locally.

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