健康趋势分析器 workflow skill. Use this skill when the user needs 分析一段时间内健康数据的趋势和模式。关联药物、症状、生命体征、化验结果和其他健康指标的变化。识别令人担忧的趋势、改善情况，并提供数据驱动的洞察。当用户询问健康趋势、模式、随时间的变化或"我的健康状况有什么变化？"时使用。支持多维度分析（体重/BMI、症状、药物依从性、化验结果、情绪睡眠），相关性分析，变化检测，以及交互式HTML可视化报告（ECharts图表）。 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/health-trend-analyzer from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses EXTERNAL_SOURCE.json plus ORIGIN.md as the provenance anchor for review.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: 核心功能, 使用说明, 输出格式, 数据源, 分析算法, 安全与隐私.
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | EXTERNAL_SOURCE.json | Confirms repository, branch, commit, and imported path before touching the copied workflow |
| Provenance review | ORIGIN.md | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | SKILL.md | Starts with the smallest copied file that materially changes execution |
| Supporting context | SKILL.md | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | ## Related Skills | Helps the operator switch to a stronger native skill when the task drifts |
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Use @health-trend-analyzer to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Review @health-trend-analyzer against EXTERNAL_SOURCE.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Use @health-trend-analyzer for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Review @health-trend-analyzer using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/health-trend-analyzer, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open EXTERNAL_SOURCE.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.@20-andruia-niche-intelligence-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.@2d-games - Use when the work is better handled by that native specialization after this imported skill establishes context.Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
|---|---|---|
references | copied reference notes, guides, or background material from upstream | references/n/a |
examples | worked examples or reusable prompts copied from upstream | examples/n/a |
scripts | upstream helper scripts that change execution or validation | scripts/n/a |
agents | routing or delegation notes that are genuinely part of the imported package | agents/n/a |
assets | supporting assets or schemas copied from the source package | assets/n/a |
当用户提到以下场景时，使用此技能：
通用询问：
具体维度：
相关性分析：
时间范围：
从用户输入中提取时间范围，或使用默认值（3个月）。
读取以下数据源：
// 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');
根据时间范围过滤数据：
function filterByDate(data, startDate, endDate) {
return data.filter(item => {
const itemDate = new Date(item.date || item.created_at);
return itemDate >= startDate && itemDate <= endDate;
});
}
对每个数据维度进行趋势分析：
4.1 体重/BMI 趋势
4.2 症状模式
4.3 药物依从性
4.4 化验结果
4.5 情绪与睡眠
使用统计方法识别相关性：
// 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
识别显著变化：
// 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
}
基于分析结果生成预测性洞察：
// 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
}
生成交互式HTML报告：
详细输出格式参见：数据源说明
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。
生成包含ECharts交互式图表的独立HTML文件，包含：
HTML文件特点：
| 数据源 | 文件路径 | 数据内容 |
|---|---|---|
| 个人档案 | data/profile.json | 体重、身高、BMI历史 |
| 症状记录 | data/symptoms/**/*.json | 症状名称、严重程度、持续时间 |
| 情绪记录 | data/mood/**/*.json | 情绪评分、睡眠质量、压力水平 |
| 饮食记录 | data/diet/**/*.json | 餐次、食物、卡路里、营养素 |
| 用药日志 | data/medication-logs/**/*.json | 用药时间、依从性记录 |
| 化验结果 | data/medical_records/**/*.json | 生化指标、参考范围 |
| 数据源 | 文件路径 | 数据内容 |
|---|---|---|
| 女性周期 | data/cycle-tracker.json | 周期长度、症状记录 |
| 孕期追踪 | data/pregnancy-tracker.json | 孕周、体重、检查记录 |
| 更年期 | data/menopause-tracker.json | 症状、HRT使用 |
| 过敏史 | data/allergies.json | 过敏原、严重程度 |
| 辐射记录 | data/radiation-records.json | 累积辐射剂量 |
详细数据结构说明请参考：data-sources.md
详细算法说明请参考：algorithms.md
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
用户："分析我的症状模式" 输出：重点分析症状频率、诱因、趋势
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
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
/symptom：记录症状/mood：记录情绪/diet：记录饮食/medication：管理药物和用药记录/query：查询特定数据点此Skill仅使用以下工具（无需额外权限）：
data/health-reports/）