Agent skill · data analytics · sickn33
ai-analyzer
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、整理风险因素、提供一般性健康信息。支持智能问答和AI健康报告生成。
Why this skill is useful
Adds complex algorithms for anomaly detection and risk prediction that the AI cannot generate on its own.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Supporting material availability This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files: <support-root>/scripts/generateaireport.py Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable. AI健康分析器 用于整理已提供的健康数据、描述趋势并准备与医生讨论的问题；这份说明不是经过临床验证的预测系统。 When to Use The user wants AI-driven health analysis across multiple health datasets or lifestyle signals. You need anomaly detection, risk prediction, or personalized recommendations based on health inputs. You need generated health reports or question-answering over health metrics and trends. 输入与证据边界 这些方法是可选分析思路，不是已实现或验证的算法。统计异常不等于医学异常；CUSUM、Z分数和相关性需要足够、可比的样本，相关不证明因果。营养摄入低于参考量不直接证明缺乏，睡眠问卷不诊断睡眠障碍。紧急症状按当地急救流程处理，避免让报告生成延误就医。 参考：ADA风险筛查、ACC风险工具。 核心功能 1. 智能健康分析 多维度数据整合: 整合基础指标、生活方式、心理健康、医疗历史等4类数据源 异常模式识别: 使用CUSUM、Z-score等算法检测异常值和变化点 相关性分析: 计算不同健康指标之间的相关性（皮尔逊、斯皮尔曼） 趋势预测: 基于历史数据进行趋势分析和预测 2. 健康风险预测 高血压风险: 基于Framingham风险评分模型 糖尿病风险: 基于ADA糖尿病风险评分标准 心血管疾病风险: 基于ACC/AHA ASCVD指南 营养缺乏风险: 基于RDA达成率和饮食模式分析 睡眠障碍风险: 基于PSQI和睡眠模式分析 3. 个性化建议引擎 基础个性化: 基于年龄、性别、BMI、活动水平等静态档案 建议分级: Level 1（一般性）、Level 2（参考性）、Level 3（医疗建议） 循证依据: 基于医学指南和循证医学证据 可操作性: 提供具体、可行的改进建议 4. 自然语言交互 智能问答: 支持健康数据查询、趋势分析、相关性查询等 上下文理解: 维护对话历史，支持多轮对话 意图识别: 识别用户查询意图，提供精准回复 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/ai-analyzer