health-trend-analyzer
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Chinese健康趋势分析器
Health Trend Analyzer
分析一段时间内健康数据的趋势和模式,识别变化、相关性,并提供数据驱动的健康洞察。
Analyze trends and patterns in health data over time, identify changes and correlations, and provide data-driven health insights.
核心功能
Core Features
1. 多维度趋势分析
1. Multi-dimensional Trend Analysis
- 体重/BMI 趋势:追踪体重和BMI随时间的变化,评估健康趋势
- 症状模式:识别反复出现的症状、频率变化、潜在诱因
- 药物依从性:分析用药规律,识别漏服模式和改善空间
- 化验结果趋势:追踪生化指标变化(胆固醇、血糖、血压等)
- 情绪与睡眠:关联情绪状态与睡眠质量,识别心理健康趋势
- Weight/BMI Trends: Track changes in weight and BMI over time to evaluate health trends
- Symptom Patterns: Identify recurring symptoms, frequency changes, and potential triggers
- Medication Adherence: Analyze medication-taking patterns, identify missed dose patterns, and find areas for improvement
- Lab Result Trends: Track changes in biochemical indicators (cholesterol, blood glucose, blood pressure, etc.)
- Mood and Sleep: Correlate emotional states with sleep quality to identify mental health trends
2. 相关性分析引擎
2. Correlation Analysis Engine
- 药物-症状相关性:识别新药物是否与症状变化相关
- 生活方式影响:关联饮食/睡眠与症状和情绪
- 治疗效果评估:衡量治疗是否导致改善
- 周期-症状相关性:女性健康追踪中的周期相关性
- Medication-Symptom Correlation: Identify if new medications are associated with symptom changes
- Lifestyle Impact: Correlate diet/sleep with symptoms and mood
- Treatment Effect Evaluation: Measure if treatments lead to improvements
- Cycle-Symptom Correlation: Cycle correlation in women's health tracking
3. 变化检测
3. Change Detection
- 显著变化:警告快速体重变化、新症状、药物变化
- 恶化模式:早期识别健康状况下降
- 改善识别:强调积极的健康变化
- 阈值警报:接近危险水平时警告(辐射、BMI极值)
- Significant Changes: Warn of rapid weight changes, new symptoms, and medication changes
- Deterioration Patterns: Early identification of declining health status
- Improvement Recognition: Highlight positive health changes
- Threshold Alerts: Warn when approaching dangerous levels (radiation, BMI extremes)
4. 预测性洞察
4. Predictive Insights
- 风险评估:基于趋势识别风险因素
- 预防建议:基于模式建议预防措施
- 早期预警:在问题变得严重之前预测
- Risk Assessment: Identify risk factors based on trends
- Prevention Recommendations: Suggest preventive measures based on patterns
- Early Warning: Predict issues before they become serious
使用说明
Usage Instructions
触发条件
Trigger Conditions
当用户提到以下场景时,使用此技能:
通用询问:
- ✅ "过去一段时间我的健康有什么变化?"
- ✅ "分析我的健康趋势"
- ✅ "我的身体状况有什么变化?"
- ✅ "健康状况总结"
具体维度:
- ✅ "我的体重/BMI有什么趋势?"
- ✅ "分析我的症状模式"
- ✅ "我的用药依从性怎么样?"
- ✅ "我的化验指标有什么变化?"
- ✅ "我的情绪和睡眠趋势"
相关性分析:
- ✅ "我的症状和什么相关?"
- ✅ "我的药物有效吗?"
- ✅ "睡眠和我的情绪有什么关系?"
时间范围:
- 默认分析过去3个月的数据
- 支持:"过去1个月"、"过去6个月"、"过去1年"
- 支持:"2025年1月至今"、"最近90天"
Use this skill when users mention the following scenarios:
General Inquiries:
- ✅ "What changes have occurred in my health over the past period?"
- ✅ "Analyze my health trends"
- ✅ "What's changed with my physical condition?"
- ✅ "Health status summary"
Specific Dimensions:
- ✅ "What's the trend of my weight/BMI?"
- ✅ "Analyze my symptom patterns"
- ✅ "How is my medication adherence?"
- ✅ "What changes are there in my lab indicators?"
- ✅ "My mood and sleep trends"
Correlation Analysis:
- ✅ "What are my symptoms related to?"
- ✅ "Is my medication effective?"
- ✅ "What's the relationship between sleep and my mood?"
Time Range:
- Default analysis of past 3 months of data
- Supported: "Past 1 month", "Past 6 months", "Past 1 year"
- Supported: "From January 2025 to present", "Last 90 days"
执行步骤
Execution Steps
步骤 1:确定分析时间范围
Step 1: Determine Analysis Time Range
从用户输入中提取时间范围,或使用默认值(3个月)。
Extract the time range from user input, or use the default value (3 months).
步骤 2:读取健康数据
Step 2: Read Health Data
读取以下数据源:
javascript
// 1. 个人档案(BMI、体重)
const profile = readFile('data/profile.json');
// 2. 症状记录
const symptomFiles = glob('data/symptoms/**/*.json');
const symptoms = readAllJson(symptomFiles);
// 3. 情绪记录
const moodFiles = glob('data/mood/**/*.json');
const moods = readAllJson(moodFiles);
// 4. 饮食记录
const dietFiles = glob('data/diet/**/*.json');
const diets = readAllJson(dietFiles);
// 5. 用药日志
const medicationLogs = glob('data/medication-logs/**/*.json');
// 6. 女性健康数据(如适用)
const cycleData = readFile('data/cycle-tracker.json');
const pregnancyData = readFile('data/pregnancy-tracker.json');
const menopauseData = readFile('data/menopause-tracker.json');
// 7. 过敏史
const allergies = readFile('data/allergies.json');
// 8. 辐射记录
const radiation = readFile('data/radiation-records.json');Read the following data sources:
javascript
// 1. 个人档案(BMI、体重)
const profile = readFile('data/profile.json');
// 2. 症状记录
const symptomFiles = glob('data/symptoms/**/*.json');
const symptoms = readAllJson(symptomFiles);
// 3. 情绪记录
const moodFiles = glob('data/mood/**/*.json');
const moods = readAllJson(moodFiles);
// 4. 饮食记录
const dietFiles = glob('data/diet/**/*.json');
const diets = readAllJson(dietFiles);
// 5. 用药日志
const medicationLogs = glob('data/medication-logs/**/*.json');
// 6. 女性健康数据(如适用)
const cycleData = readFile('data/cycle-tracker.json');
const pregnancyData = readFile('data/pregnancy-tracker.json');
const menopauseData = readFile('data/menopause-tracker.json');
// 7. 过敏史
const allergies = readFile('data/allergies.json');
// 8. 辐射记录
const radiation = readFile('data/radiation-records.json');步骤 3:数据过滤
Step 3: Data Filtering
根据时间范围过滤数据:
javascript
function filterByDate(data, startDate, endDate) {
return data.filter(item => {
const itemDate = new Date(item.date || item.created_at);
return itemDate >= startDate && itemDate <= endDate;
});
}Filter data based on the time range:
javascript
function filterByDate(data, startDate, endDate) {
return data.filter(item => {
const itemDate = new Date(item.date || item.created_at);
return itemDate >= startDate && itemDate <= endDate;
});
}步骤 4:趋势分析
Step 4: Trend Analysis
对每个数据维度进行趋势分析:
4.1 体重/BMI 趋势
- 提取历史体重数据
- 计算BMI变化
- 识别趋势方向(上升/下降/稳定)
- 评估变化幅度
4.2 症状模式
- 统计症状频率
- 识别高频症状
- 分析症状时间模式
- 检测症状诱因
4.3 药物依从性
- 计算总体依从率
- 分析各药物依从性
- 识别漏服模式
- 评估改善建议
4.4 化验结果
- 追踪多次报告中的生化指标
- 与参考范围对比
- 识别改善/恶化
- 标记异常指标
4.5 情绪与睡眠
- 关联情绪评分与睡眠时长
- 识别情绪波动模式
- 检测压力水平
- 评估心理健康趋势
Perform trend analysis for each data dimension:
4.1 Weight/BMI Trends
- Extract historical weight data
- Calculate BMI changes
- Identify trend direction (upward/downward/stable)
- Evaluate change magnitude
4.2 Symptom Patterns
- Count symptom frequencies
- Identify high-frequency symptoms
- Analyze symptom time patterns
- Detect symptom triggers
4.3 Medication Adherence
- Calculate overall adherence rate
- Analyze adherence for each medication
- Identify missed dose patterns
- Evaluate improvement suggestions
4.4 Lab Results
- Track biochemical indicators across multiple reports
- Compare with reference ranges
- Identify improvements/deterioration
- Mark abnormal indicators
4.5 Mood and Sleep
- Correlate mood scores with sleep duration
- Identify mood fluctuation patterns
- Detect stress levels
- Evaluate mental health trends
步骤 5:相关性分析
Step 5: Correlation Analysis
使用统计方法识别相关性:
javascript
// 皮尔逊相关系数
function pearsonCorrelation(x, y) {
// 计算相关系数
// 返回值范围:-1(负相关)到 1(正相关)
}
// 应用场景
- 药物开始日期 vs 症状频率
- 睡眠时长 vs 情绪评分
- 体重变化 vs 饮食记录
- 运动量 vs 情绪状态Use statistical methods to identify correlations:
javascript
// 皮尔逊相关系数
function pearsonCorrelation(x, y) {
// 计算相关系数
// 返回值范围:-1(负相关)到 1(正相关)
}
// 应用场景
- 药物开始日期 vs 症状频率
- 睡眠时长 vs 情绪评分
- 体重变化 vs 饮食记录
- 运动量 vs 情绪状态步骤 6:变化检测
Step 6: Change Detection
识别显著变化:
javascript
// 变化点检测
function detectChangePoints(timeSeries) {
// 使用统计方法检测显著变化点
// 例如:体重突然下降、症状突然增加
}
// 阈值警报
function checkThresholds(value, thresholds) {
// 检查是否接近或超过危险阈值
// 例如:BMI > 30、辐射剂量 > 安全限
}Identify significant changes:
javascript
// 变化点检测
function detectChangePoints(timeSeries) {
// 使用统计方法检测显著变化点
// 例如:体重突然下降、症状突然增加
}
// 阈值警报
function checkThresholds(value, thresholds) {
// 检查是否接近或超过危险阈值
// 例如:BMI > 30、辐射剂量 > 安全限
}步骤 7:生成洞察
Step 7: Generate Insights
基于分析结果生成预测性洞察:
javascript
// 风险评估
function assessRisks(trends) {
// 识别高风险趋势
// 例如:快速体重下降、频繁症状
}
// 预防建议
function generateRecommendations(trends, correlations) {
// 基于模式建议预防措施
// 例如:改善睡眠、提高用药依从性
}
// 早期预警
function earlyWarnings(trends) {
// 在问题变得严重之前预测
// 例如:症状频率上升、情绪持续低落
}Generate predictive insights based on analysis results:
javascript
// 风险评估
function assessRisks(trends) {
// 识别高风险趋势
// 例如:快速体重下降、频繁症状
}
// 预防建议
function generateRecommendations(trends, correlations) {
// 基于模式建议预防措施
// 例如:改善睡眠、提高用药依从性
}
// 早期预警
function earlyWarnings(trends) {
// 在问题变得严重之前预测
// 例如:症状频率上升、情绪持续低落
}步骤 8:生成可视化报告
Step 8: Generate Visualization Report
生成交互式HTML报告:
- 数据汇总:生成JSON格式的分析结果
- HTML模板渲染:将数据注入HTML模板
- ECharts图表配置:配置6种交互式图表
- 保存文件:保存为独立HTML文件
详细输出格式参见:数据源说明
Generate an interactive HTML report:
- Data Summary: Generate analysis results in JSON format
- HTML Template Rendering: Inject data into the HTML template
- ECharts Chart Configuration: Configure 6 types of interactive charts
- Save File: Save as a standalone HTML file
Detailed output format refers to: Data Sources Description
输出格式
Output Formats
文本报告(简洁版)
Text Report (Concise Version)
健康趋势分析报告
━━━━━━━━━━━━━━━━━━━━━━━━━━
生成时间: 2025-12-31
分析周期: 过去3个月 (2025-10-01 至 2025-12-31)
📊 总体评估
━━━━━━━━━━━━━━━━━━━━━━━━━━
改善中: 体重管理、胆固醇水平
稳定: 血糖控制、情绪状态
需关注: 用药依从性、睡眠质量
📊 体重/BMI 趋势
├─ 当前体重: 68.5 kg
├─ 当前 BMI: 23.1(正常范围)
├─ 3个月变化: -2.3 kg(-3.2%)
├─ 趋势: 📉 逐渐减重
└─ 评估: ✅ 积极趋势,在健康范围内
💊 药物依从性
├─ 当前药物: 3种
├─ 总体依从率: 78%
├─ 漏服次数: 8次
├─ 最好: 阿司匹林 (95%)
└─ 需改进: 氨氯地平 (65%)
⚠️ 症状模式
├─ 最频繁: 头痛(过去3个月 12次)
├─ 趋势: 📉 频率下降(较上期减少4次)
├─ 潜在诱因: 与睡眠质量识别出中等相关(r=0.62)
└─ 建议: 继续改善睡眠模式
🧪 化验结果趋势
├─ 胆固醇: 240 → 210 mg/dL(改善 ✅)
├─ 血糖: 5.6 → 5.4 mmol/L(稳定)
├─ 上次检查: 30天前
└─ 建议: 3个月后复查
😊 情绪与睡眠
├─ 平均情绪评分: 6.8/10
├─ 平均睡眠时长: 6.5小时
├─ 趋势: 情绪稳定,睡眠略有改善
└─ 相关性: 睡眠时长与情绪评分强相关(r=0.78)
🔗 相关性分析
━━━━━━━━━━━━━━━━━━━━━━━━━━
• 睡眠时长 ↔ 情绪评分: 强正相关 (r=0.78)
• 体重变化 ↔ 饮食记录: 中等相关 (r=0.55)
• 用药依从性 ↔ 症状频率: 中等负相关 (r=-0.62)
💡 风险评估与建议
━━━━━━━━━━━━━━━━━━━━━━━━━━
🟢 继续保持
• 当前体重管理方法有效
• 胆固醇水平改善明显
🟡 需要关注
• 提高氨氯地平依从性(设置提醒)
• 增加睡眠时长至7-8小时
📅 复查计划
• 3个月后复查血脂四项
• 1个月后评估用药依从性改善
━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ 免责声明
本分析仅供参考,不替代专业医疗诊断。
请咨询医生获取专业建议。Health Trend Analysis Report
━━━━━━━━━━━━━━━━━━━━━━━━━━
Generation Time: 2025-12-31
Analysis Period: Past 3 months (2025-10-01 to 2025-12-31)
📊 Overall Assessment
━━━━━━━━━━━━━━━━━━━━━━━━━━
Improving: Weight management, cholesterol levels
Stable: Blood glucose control, emotional state
Needs Attention: Medication adherence, sleep quality
📊 Weight/BMI Trends
├─ Current Weight: 68.5 kg
├─ Current BMI: 23.1 (Normal Range)
├─ 3-month Change: -2.3 kg (-3.2%)
├─ Trend: 📉 Gradual weight loss
└─ Assessment: ✅ Positive trend, within healthy range
💊 Medication Adherence
├─ Current Medications: 3 types
├─ Overall Adherence Rate: 78%
├─ Missed Doses: 8 times
├─ Best: Aspirin (95%)
└─ Needs Improvement: Amlodipine (65%)
⚠️ Symptom Patterns
├─ Most Frequent: Headache (12 times in past 3 months)
├─ Trend: 📉 Decreasing frequency (4 fewer than previous period)
├─ Potential Trigger: Moderate correlation identified with sleep quality (r=0.62)
└─ Recommendation: Continue improving sleep patterns
🧪 Lab Result Trends
├─ Cholesterol: 240 → 210 mg/dL (Improved ✅)
├─ Blood Glucose: 5.6 → 5.4 mmol/L (Stable)
├─ Last Check: 30 days ago
└─ Recommendation: Recheck in 3 months
😊 Mood and Sleep
├─ Average Mood Score: 6.8/10
├─ Average Sleep Duration: 6.5 hours
├─ Trend: Stable mood, slight improvement in sleep
└─ Correlation: Strong correlation between sleep duration and mood score (r=0.78)
🔗 Correlation Analysis
━━━━━━━━━━━━━━━━━━━━━━━━━━
• Sleep Duration ↔ Mood Score: Strong positive correlation (r=0.78)
• Weight Change ↔ Diet Records: Moderate correlation (r=0.55)
• Medication Adherence ↔ Symptom Frequency: Moderate negative correlation (r=-0.62)
💡 Risk Assessment and Recommendations
━━━━━━━━━━━━━━━━━━━━━━━━━━
🟢 Keep Up the Good Work
• Current weight management methods are effective
• Cholesterol levels have improved significantly
🟡 Needs Attention
• Improve amlodipine adherence (set reminders)
• Increase sleep duration to 7-8 hours
📅 Follow-up Plan
• Recheck lipid panel in 3 months
• Evaluate medication adherence improvement in 1 month
━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ Disclaimer
This analysis is for reference only and does not replace professional medical diagnosis.
Please consult a doctor for professional advice.HTML可视化报告(完整版)
HTML Visualization Report (Full Version)
生成包含ECharts交互式图表的独立HTML文件,包含:
- 总体评估卡片:关键指标一目了然
- 体重/BMI趋势图:双Y轴折线图(体重 + BMI)
- 症状频率图:颜色编码的柱状图(高频红/中频黄/低频绿)
- 药物依从性仪表盘:总体依从率 + 各药物详情
- 化验结果趋势图:多系列折线图 + 参考线
- 相关性热图:热力图展示变量间相关性
- 情绪与睡眠面积图:双Y轴面积图
HTML文件特点:
- ✅ 完全独立(所有依赖通过CDN)
- ✅ 交互式图表(缩放、导出、图例切换)
- ✅ 响应式设计(移动端适配)
- ✅ 可打印(打印优化样式)
- ✅ 可分享(发送给医生)
Generate a standalone HTML file with ECharts interactive charts, including:
- Overall Assessment Card: Key indicators at a glance
- Weight/BMI Trend Chart: Dual Y-axis line chart (weight + BMI)
- Symptom Frequency Chart: Color-coded bar chart (high frequency red/medium frequency yellow/low frequency green)
- Medication Adherence Dashboard: Overall adherence rate + details for each medication
- Lab Result Trend Chart: Multi-series line chart + reference lines
- Correlation Heatmap: Heatmap showing correlations between variables
- Mood and Sleep Area Chart: Dual Y-axis area chart
HTML File Features:
- ✅ Fully standalone (all dependencies via CDN)
- ✅ Interactive charts (zoom, export, legend toggle)
- ✅ Responsive design (mobile-adapted)
- ✅ Printable (print-optimized styles)
- ✅ Shareable (send to doctors)
数据源
Data Sources
主要数据源
Primary Data Sources
| 数据源 | 文件路径 | 数据内容 |
|---|---|---|
| 个人档案 | | 体重、身高、BMI历史 |
| 症状记录 | | 症状名称、严重程度、持续时间 |
| 情绪记录 | | 情绪评分、睡眠质量、压力水平 |
| 饮食记录 | | 餐次、食物、卡路里、营养素 |
| 用药日志 | | 用药时间、依从性记录 |
| 化验结果 | | 生化指标、参考范围 |
| Data Source | File Path | Data Content |
|---|---|---|
| Personal Profile | | Weight, height, BMI history |
| Symptom Records | | Symptom name, severity, duration |
| Mood Records | | Mood score, sleep quality, stress level |
| Diet Records | | Meals, food, calories, nutrients |
| Medication Logs | | Medication time, adherence records |
| Lab Results | | Biochemical indicators, reference ranges |
辅助数据源
Secondary Data Sources
| 数据源 | 文件路径 | 数据内容 |
|---|---|---|
| 女性周期 | | 周期长度、症状记录 |
| 孕期追踪 | | 孕周、体重、检查记录 |
| 更年期 | | 症状、HRT使用 |
| 过敏史 | | 过敏原、严重程度 |
| 辐射记录 | | 累积辐射剂量 |
详细数据结构说明请参考:data-sources.md
| Data Source | File Path | Data Content |
|---|---|---|
| Women's Cycle | | Cycle length, symptom records |
| Pregnancy Tracker | | Gestational week, weight, checkup records |
| Menopause | | Symptoms, HRT usage |
| Allergy History | | Allergens, severity |
| Radiation Records | | Cumulative radiation dose |
Detailed data structure description refers to: data-sources.md
分析算法
Analysis Algorithms
时间序列分析
Time Series Analysis
- 趋势检测(线性回归)
- 季节性分析
- 异常值检测
- Trend detection (linear regression)
- Seasonal analysis
- Outlier detection
相关性分析
Correlation Analysis
- 皮尔逊相关系数(连续变量)
- 斯皮尔曼相关系数(有序变量)
- 交叉相关分析(时间序列)
- Pearson correlation coefficient (continuous variables)
- Spearman correlation coefficient (ordinal variables)
- Cross-correlation analysis (time series)
变化点检测
Change Point Detection
- CUSUM算法
- 滑动窗口t检验
- 贝叶斯变化点检测
- CUSUM algorithm
- Sliding window t-test
- Bayesian change point detection
统计指标
Statistical Indicators
- 均值、中位数、标准差
- 百分位数(25%, 50%, 75%)
- 变化率(环比、同比)
详细算法说明请参考:algorithms.md
- Mean, median, standard deviation
- Percentiles (25%, 50%, 75%)
- Change rates (month-over-month, year-over-year)
Detailed algorithm description refers to: algorithms.md
安全与隐私
Safety and Privacy
必须遵循
Must Follow
- ❌ 不给出医疗诊断
- ❌ 不给出具体用药建议
- ❌ 不判断生死预后
- ❌ 标注免责声明(仅供参考)
- ❌ Do not provide medical diagnoses
- ❌ Do not provide specific medication advice
- ❌ Do not judge life-or-death prognosis
- ❌ Include a disclaimer (for reference only)
信息准确度
Information Accuracy
- ✅ 仅基于已记录的数据进行分析
- ✅ 不推测或推断缺失信息
- ✅ 明确标注数据来源和时间范围
- ✅ 建议应由医疗专业人员审查
- ✅ Only analyze based on recorded data
- ✅ Do not speculate or infer missing information
- ✅ Clearly label data sources and time ranges
- ✅ Recommendations should be reviewed by medical professionals
隐私保护
Privacy Protection
- ✅ 所有数据保持本地
- ✅ 无外部API调用
- ✅ 分析结果仅保存在本地
- ✅ HTML报告独立运行(无数据传输)
- ✅ All data remains local
- ✅ No external API calls
- ✅ Analysis results are only stored locally
- ✅ HTML reports run independently (no data transmission)
错误处理
Error Handling
数据缺失
Data Missing
- 无数据:输出"暂无数据,建议先记录[数据类型]"
- 数据不足:输出"数据不足(需要至少1个月数据才能进行趋势分析)"
- 数据范围窄:使用现有数据,提示"建议延长记录时间以获得更准确的趋势"
- No Data: Output "No data available. It is recommended to record [data type] first"
- Insufficient Data: Output "Insufficient data (at least 1 month of data is required for trend analysis)"
- Narrow Data Range: Use existing data and prompt "It is recommended to extend the recording time for more accurate trends"
分析失败
Analysis Failure
- 无法计算趋势:输出"无法计算趋势,数据点不足"
- 相关性分析失败:输出"相关性分析需要更多数据"
- 图表渲染失败:降级为文本报告
- Unable to Calculate Trends: Output "Unable to calculate trends due to insufficient data points"
- Correlation Analysis Failure: Output "More data is needed for correlation analysis"
- Chart Rendering Failure: Degrade to text report
使用示例
Usage Examples
示例 1:一般健康趋势
Example 1: General Health Trends
用户:"过去3个月我的健康有什么变化?"
输出:生成完整的HTML报告,包含所有维度的趋势分析
User: "What changes have occurred in my health over the past 3 months?"
Output: Generate a complete HTML report with trend analysis across all dimensions
示例 2:症状分析
Example 2: Symptom Analysis
用户:"分析我的症状模式"
输出:重点分析症状频率、诱因、趋势
User: "Analyze my symptom patterns"
Output: Focus on symptom frequency, triggers, and trends
示例 3:体重趋势
Example 3: Weight Trends
用户:"我的体重有什么趋势?"
输出:重点分析体重/BMI变化、与饮食/运动的相关性
User: "What's the trend of my weight?"
Output: Focus on weight/BMI changes and correlations with diet/exercise
示例 4:药物有效性
Example 4: Medication Effectiveness
用户:"我的降压药有效吗?"
输出:关联药物开始日期与血压读数、症状改善
更多完整示例请参考:examples.md
User: "Is my blood pressure medication effective?"
Output: Correlate medication start date with blood pressure readings and symptom improvements
More complete examples refer to: examples.md
相关命令
Related Commands
- :记录症状
/symptom - :记录情绪
/mood - :记录饮食
/diet - :管理药物和用药记录
/medication - :查询特定数据点
/query
- : Record symptoms
/symptom - : Record mood
/mood - : Record diet
/diet - : Manage medications and medication records
/medication - : Query specific data points
/query
技术实现
Technical Implementation
工具限制
Tool Limitations
此Skill仅使用以下工具(无需额外权限):
- Read:读取JSON数据文件
- Grep:搜索特定模式
- Glob:按模式查找数据文件
- Write:生成HTML报告(保存到)
data/health-reports/
This Skill only uses the following tools (no additional permissions required):
- Read: Read JSON data files
- Grep: Search for specific patterns
- Glob: Find data files by pattern
- Write: Generate HTML reports (save to )
data/health-reports/
性能优化
Performance Optimization
- 增量读取:仅读取指定时间范围的数据文件
- 数据缓存:避免重复读取同一文件
- 延迟计算:按需生成图表数据
- Incremental reading: Only read data files within the specified time range
- Data caching: Avoid re-reading the same file
- Lazy calculation: Generate chart data on demand
扩展性
Extensibility
- 支持添加新的数据维度
- 支持自定义图表类型
- 支持自定义分析算法
- Support adding new data dimensions
- Support custom chart types
- Support custom analysis algorithms