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Get Started Free →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。
.claude/skills/ai-analyzer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | — | — |
| case-23 | ✗→✓ | ▲ Improved | — | — |
| case-16 | ✗→✓ | ▲ Improved | — | — |
| case-15 | ✗→✓ | ▲ Improved | — | — |
| case-21 | ✗→✓ | ▲ Improved | — | — |
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:
javascriptconst 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。
javascriptconst 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：
javascript// 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：
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-13 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-23 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 16 counted toward the lift figure. The other 7 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +43 percentage points is the difference between those two pass rates over the 16 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.