AI健康分析器 workflow skill. Use this skill when the user needs 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。 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/skills/ai-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, 数据源, 算法说明, 安全与合规, 相关命令.
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 @ai-analyzer-v2 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 @ai-analyzer-v2 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 @ai-analyzer-v2 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 @ai-analyzer-v2 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/skills/ai-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:
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：