[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CT2NS6N6ZAP7B2PVC2QKJH":3,"skill-same:01M6CT2NS6N6ZAP7B2PVC2QKJH":55},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":37,"ownerHtmlUrl":38,"ownerBlogUrl":6,"ownerDescription":39,"updatedAt":40,"install":41,"resources":54},"01M6CT2NS6N6ZAP7B2PVC2QKJH","family-health-analyzer",null,"分析家族病史、评估遗传风险、识别家庭健康模式、提供个性化预防建议","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Ffamily-health-analyzer\u002FSKILL.md",[],[19,20,21,22,23,24,25],"health","family","family health analyzer","grep","read","write","community","2026-11-10 10:12:30",false,"---\nname: family-health-analyzer\ndescription: 分析家族病史、评估遗传风险、识别家庭健康模式、提供个性化预防建议\nallowed-tools: Read, Write, Grep, Glob\nrisk: unknown\nsource: community\n---\n\n# 家庭健康分析技能\n\n## When to Use\n\n- 需要分析家族病史、遗传风险或家庭层面的健康模式时使用。\n- 任务涉及家庭健康报告、家族聚集性疾病识别或预防建议生成。\n- 需要把多个家庭成员的健康数据汇总后做趋势或风险评估。\n\n## 技能概述\n\n本技能提供家庭健康数据的深度分析,包括:\n- 遗传风险评估\n- 家族疾病模式识别\n- 家庭共同问题分析\n- 个性化预防建议\n- 可视化报告生成\n\n## 触发条件\n\n当用户请求以下内容时,使用此技能:\n- \"家庭健康报告\"\n- \"家族病史分析\"\n- \"遗传风险评估\"\n- \"家庭健康趋势\"\n- 执行 `\u002Ffamily report` 命令\n- 执行 `\u002Ffamily risk` 命令\n\n## 分析步骤\n\n### 步骤1: 确定分析目标\n\n识别用户请求类型:\n- 家族病史分析\n- 遗传风险评估\n- 家庭健康趋势\n- 家庭健康报告\n\n### 步骤2: 读取家庭数据\n\n**数据源:**\n1. 主数据文件: `data\u002Ffamily-health-tracker.json`\n2. 集成模块数据:\n   - `data\u002Fhypertension-tracker.json`\n   - `data\u002Fdiabetes-tracker.json`\n   - `data\u002Fprofile.json`\n\n### 步骤3: 数据验证与清洗\n\n**验证项目:**\n- 关系完整性\n- 年龄合理性\n- 数据一致性\n\n### 步骤4: 遗传模式识别\n\n**识别算法:**\n1. 家族聚集性分析\n2. 遗传模式识别\n3. 早发病例识别(通常\u003C50岁)\n\n### 步骤5: 风险计算算法\n\n**加权计算:**\n```python\n遗传风险评分 = (一级亲属患病数 × 0.4) +\n              (早发病例数 × 0.3) +\n              (家族聚集度 × 0.3)\n\n风险等级:\n- 高风险: ≥70%\n- 中风险: 40%-69%\n- 低风险: \u003C40%\n```\n\n### 步骤6: 生成预防建议\n\n**建议分类:**\n- 筛查建议:定期检查项目\n- 生活方式建议:饮食、运动、作息\n- 就医建议:何时就医、咨询专科\n\n**示例:**\n```json\n{\n  \"category\": \"screening\",\n  \"action\": \"定期血压监测\",\n  \"frequency\": \"每周3次\",\n  \"start_age\": 35,\n  \"priority\": \"high\"\n}\n```\n\n### 步骤7: 生成可视化报告\n\n**HTML报告组件:**\n1. 家谱树(ECharts树图)\n2. 遗传风险热力图\n3. 疾病分布饼图\n4. 预防建议时间线\n\n### 步骤8: 输出结果\n\n**输出格式:**\n1. 文本报告(简洁版):命令行输出\n2. HTML报告(完整版):可视化图表\n\n## 安全原则\n\n### 医学安全边界\n- ✅ 仅基于家族病史进行统计分析\n- ✅ 提供预防建议和筛查提醒\n- ✅ 明确标注不确定性\n- ❌ 不进行遗传疾病诊断\n- ❌ 不预测个体发病概率\n- ❌ 不推荐具体治疗方案\n\n### 免责声明\n每次分析输出必须包含:\n```\n⚠️ 免责声明:\n1. 本分析基于家族病史统计,仅供参考\n2. 遗传风险评估不预测个体发病\n3. 所有医疗决策请咨询专业医师\n4. 遗传咨询建议咨询专业遗传咨询师\n```\n\n## 集成现有模块\n\n- 读取高血压管理数据\n- 读取糖尿病管理数据\n- 关联用药记录\n\n---\n\n**技能版本**: v1.0\n**最后更新**: 2025-01-08\n**维护者**: WellAlly Tech\n","# 家庭健康分析技能\n\n## When to Use\n\n- 需要分析家族病史、遗传风险或家庭层面的健康模式时使用。\n- 任务涉及家庭健康报告、家族聚集性疾病识别或预防建议生成。\n- 需要把多个家庭成员的健康数据汇总后做趋势或风险评估。\n\n## 技能概述\n\n本技能提供家庭健康数据的深度分析,包括:\n- 遗传风险评估\n- 家族疾病模式识别\n- 家庭共同问题分析\n- 个性化预防建议\n- 可视化报告生成\n\n## 触发条件\n\n当用户请求以下内容时,使用此技能:\n- \"家庭健康报告\"\n- \"家族病史分析\"\n- \"遗传风险评估\"\n- \"家庭健康趋势\"\n- 执行 `\u002Ffamily report` 命令\n- 执行 `\u002Ffamily risk` 命令\n\n## 分析步骤\n\n### 步骤1: 确定分析目标\n\n识别用户请求类型:\n- 家族病史分析\n- 遗传风险评估\n- 家庭健康趋势\n- 家庭健康报告\n\n### 步骤2: 读取家庭数据\n\n**数据源:**\n1. 主数据文件: `data\u002Ffamily-health-tracker.json`\n2. 集成模块数据:\n   - `data\u002Fhypertension-tracker.json`\n   - `data\u002Fdiabetes-tracker.json`\n   - `data\u002Fprofile.json`\n\n### 步骤3: 数据验证与清洗\n\n**验证项目:**\n- 关系完整性\n- 年龄合理性\n- 数据一致性\n\n### 步骤4: 遗传模式识别\n\n**识别算法:**\n1. 家族聚集性分析\n2. 遗传模式识别\n3. 早发病例识别(通常\u003C50岁)\n\n### 步骤5: 风险计算算法\n\n**加权计算:**\n```python\n遗传风险评分 = (一级亲属患病数 × 0.4) +\n              (早发病例数 × 0.3) +\n              (家族聚集度 × 0.3)\n\n风险等级:\n- 高风险: ≥70%\n- 中风险: 40%-69%\n- 低风险: \u003C40%\n```\n\n### 步骤6: 生成预防建议\n\n**建议分类:**\n- 筛查建议:定期检查项目\n- 生活方式建议:饮食、运动、作息\n- 就医建议:何时就医、咨询专科\n\n**示例:**\n```json\n{\n  \"category\": \"screening\",\n  \"action\": \"定期血压监测\",\n  \"frequency\": \"每周3次\",\n  \"start_age\": 35,\n  \"priority\": \"high\"\n}\n```\n\n### 步骤7: 生成可视化报告\n\n**HTML报告组件:**\n1. 家谱树(ECharts树图)\n2. 遗传风险热力图\n3. 疾病分布饼图\n4. 预防建议时间线\n\n### 步骤8: 输出结果\n\n**输出格式:**\n1. 文本报告(简洁版):命令行输出\n2. 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