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AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。
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Based on SOC occupation classification
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Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
| name | ai-analyzer |
| description | AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。 |
| allowed-tools | Read, Grep, Glob, Write |
基于AI技术的综合健康分析系统，提供智能健康洞察、风险预测和个性化建议。
当用户提到以下场景时，使用此技能：
通用询问:
风险预测:
智能问答:
报告生成:
const aiConfig = readFile('data/ai-config.json');
const aiHistory = readFile('data/ai-history.json');
检查AI功能是否启用，验证数据源配置。
const profile = readFile('data/profile.json');
获取基础信息：年龄、性别、身高、体重、BMI等。
根据配置的数据源读取相关数据：
// 基础健康指标
const indexData = readFile('data/index.json');
// 生活方式数据
const fitnessData = readFile('data-example/fitness-tracker.json');
const sleepData = readFile('data-example/sleep-tracker.json');
const nutritionData = readFile('data-example/nutrition-tracker.json');
// 心理健康数据
const mentalHealthData = readFile('data-example/mental-health-tracker.json');
// 医疗历史
const medications = exists('data/medications.json') ? readFile('data/medications.json') : null;
const allergies = exists('data/allergies.json') ? readFile('data/allergies.json') : null;
整合所有数据源，进行数据清洗、时间对齐和缺失值处理。
相关性分析: 计算睡眠↔情绪、运动↔体重、营养↔生化指标等关联
趋势分析: 使用线性回归、移动平均等方法识别趋势方向
异常检测: 使用CUSUM、Z-score算法检测异常值和变化点
基于Framingham、ADA、ACC/AHA等标准进行风险预测：
根据分析结果生成三级建议：
文本报告: 包含总体评估、风险预测、关键趋势、相关性发现、个性化建议
HTML报告: 调用 scripts/generate_ai_report.py 生成包含ECharts图表的交互式报告
记录分析结果到 data/ai-history.json
| 数据源 | 文件路径 | 数据内容 |
|---|---|---|
| 用户档案 | data/profile.json | 年龄、性别、身高、体重、BMI |
| 医疗记录 | data/index.json | 生化指标、影像检查 |
| 运动追踪 | data-example/fitness-tracker.json | 运动类型、时长、强度、MET值 |
| 睡眠追踪 | data-example/sleep-tracker.json | 睡眠时长、质量、PSQI评分 |
| 营养追踪 | data-example/nutrition-tracker.json | 饮食记录、营养素摄入、RDA达成率 |
| 心理健康 | data-example/mental-health-tracker.json | PHQ-9、GAD-7评分 |
| 用药记录 | data/medications.json | 药物名称、剂量、用法、依从性 |
| 过敏史 | data/allergies.json | 过敏原、严重程度 |
/ai analyze - AI综合分析/ai predict [risk_type] - 健康风险预测/ai chat [query] - 自然语言问答/ai report generate [type] - 生成AI健康报告/ai status - 查看AI功能状态此Skill仅使用以下工具：