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Health Data Analyzer

Name: Health Data Analyzer
Author: tianchuanjun64-afk

作者 tianchuanjun64-afk · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

332

总下载

当前安装

版本数

在 OpenClaw 中安装

/install health-data-analyzer

功能描述

健康数据分析专家，通过 mcporter 访问 healthdata MCP 服务器进行睡眠、运动、恢复等健康数据的查询和分析。当用户询问健康数据、睡眠质量、运动表现、身体恢复状态、心率变异性、血氧水平等健康相关问题时使用此技能。支持多维度健康数据分析、趋势分析、个性化健康建议。

使用说明 (SKILL.md)

健康数据分析器

专业的健康数据分析工具，通过 MCP 服务器访问完整的健康数据库，提供睡眠、运动、恢复等多维度健康分析。

核心功能

睡眠分析: 睡眠质量评分、睡眠分期分析、睡眠债务计算
运动分析: 运动负荷评估、训练效果分析、心率区间分析
恢复分析: 身体恢复状态评估、HRV/RHR/血氧/体温综合分析
趋势分析: 长期健康趋势、个性化基线对比
多设备融合: 支持多设备数据融合分析

数据访问流程

标准三步流程

所有健康数据查询必须遵循以下三步流程：

列出可用表 - 了解数据库结构
获取表结构 - 理解字段定义
查询数据 - 获取实际数据进行分析

基础命令

1. 列出所有数据表

mcporter call healthdata.list_available_tables

2. 获取表字段结构

mcporter call healthdata.get_table_schema table_list='["table1", "table2"]'

参数格式:

table_list: JSON 数组格式，用单引号包围
示例: table_list='["sleep_segments", "sleep_calculations"]'

3. 查询表数据

mcporter call healthdata.query_table_data table_name=TABLE_NAME start_date=YYYY-MM-DD end_date=YYYY-MM-DD conversation_time="YYYY-MM-DD HH:MM:SS"

参数说明:

table_name: 要查询的表名
start_date: 开始日期 (YYYY-MM-DD)
end_date: 结束日期 (YYYY-MM-DD)
conversation_time: 当前对话时间，用引号包围

数据表概览

用户与设备表

users: 用户基础信息
user_data_sources: 数据源设备信息

原始数据表

health_data_numeric: 原始多设备健康数据
fusion_health_data_numeric: 融合后统一健康数据
health_data_workout: 原始运动数据

分段汇总表

sleep_segments: 睡眠分段数据 (一晚完整睡眠)
training_segments: 训练分段数据 (一次完整运动)
metrics_segments: 健康指标分段数据

评分计算表

sleep_calculations: 睡眠质量评分
strain_calculations: 运动负荷评分
recovery_calculations: 身体恢复评分

分析工作流程

睡眠分析流程

查询 sleep_segments 获取睡眠基础数据
查询 sleep_calculations 获取睡眠评分
结合数据进行睡眠质量分析和建议

运动分析流程

查询 training_segments 获取运动数据
查询 strain_calculations 获取负荷评分
分析运动表现和训练建议

恢复分析流程

查询 recovery_calculations 获取恢复评分
查询 metrics_segments 获取生理指标
综合分析身体恢复状态

时间范围建议

短期分析: 最近 7-14 天
趋势分析: 最近 30-90 天
基线对比: 建议向前延伸时间范围以捕获更多历史数据

数据质量检查

检查 data_quality_flag 字段 (normal 为正常)
注意设备数据源的一致性
识别数据缺失或异常值

分析输出格式

基础数据摘要

数据时间范围和记录数量
数据质量状态
主要设备来源

核心指标分析

关键健康指标的当前值和趋势
与个人基线的对比
异常值识别和说明

个性化建议

基于数据分析的健康建议
改进方向和具体措施
需要关注的健康风险

常见查询模式

详细的查询模式和示例请参考 references/query-patterns.md

数据库架构

完整的数据库表结构和字段说明请参考 references/database-schema.md

故障排除

如果 mcporter 命令失败：

检查 healthdata 服务器状态: mcporter list
验证参数格式，特别是数组和日期格式
确认表名和字段名的正确性
检查时间范围的合理性

安全使用建议

This skill appears to do what its description says — it calls the local mcporter CLI to query a healthdata MCP server and analyze sleep/exercise/recovery metrics. Key things to consider before installing: - Sensitive data: the skill will query tables containing PII and health data (emails, user IDs, HRV, SPO2, sleep stages). Only enable it if you trust the MCP server and agree that this data may be accessed. - Implicit auth: the skill doesn't request credentials because it relies on the system's mcporter configuration; review mcporter's auth/config to ensure it has least-privilege access. - Local trust: scripts call the mcporter binary and jq; ensure mcporter on your system is the expected binary (not a malicious replacement) and run check_status.sh in a controlled environment first. - Injection/command risk: the Python code builds mcporter CLI arguments and passes them to subprocess.run without a shell (lower risk), but inputs should still be validated by the caller — avoid passing untrusted arbitrary strings into query parameters. If you need stronger assurance, ask the publisher for the service endpoint and authentication model, or run the scripts in a sandboxed environment and inspect mcporter configuration and server ACLs before granting access.

功能分析

Type: OpenClaw Skill Name: health-data-analyzer Version: 1.0.0 The skill bundle provides a framework for analyzing sensitive health data via an MCP server, but it contains a command injection vulnerability in scripts/health_analyzer.py. The script constructs shell commands using f-strings and str.split() before execution via subprocess.run, which could allow for argument injection if the AI agent is supplied with malicious inputs. While the behavior aligns with the stated purpose of health analysis and no evidence of intentional exfiltration was found, the combination of unsafe command construction and access to private health databases (including sleep, heart rate, and physiological metrics) warrants a suspicious classification.

能力评估

✓ Purpose & Capability

The name/description (health data analysis) match the SKILL.md and the bundled scripts: both the shell script and Python script invoke the mcporter CLI to list tables, get schemas, and query table data from a 'healthdata' MCP server. There are no unrelated dependencies, credentials, or install steps requested that don't fit the stated purpose.

ℹ Instruction Scope

SKILL.md explicitly instructs the agent to list tables, fetch schemas, and query table data — exactly what a health-data analyzer should do. However the documented and code-accessed tables include 'users' and fields such as email/external_id and many health indicators (HRV, SPO2, sleep stages). That means the skill will access PII and sensitive health data (PHI). The instructions do not direct data to any external endpoint beyond the mcporter/healthdata service.

✓ Install Mechanism

There is no install spec (instruction-only), and included files are local scripts. Nothing is downloaded from external URLs or extracted. Risk from the install mechanism is low — code will only be written from the provided bundle.

ℹ Credentials

The skill declares no required environment variables or credentials, which is coherent if mcporter handles auth via its own configuration. This can be surprising: the skill will operate with whatever permissions mcporter (and the host) already have, and can therefore access full user records and sensitive health metrics. Ensure mcporter's credentials/config are appropriately scoped before allowing the skill to run.

✓ Persistence & Privilege

always is false and the skill does not request persistent system privileges or modify other skills. It runs on-demand code (shell script and Python) and does not attempt to change agent-wide settings.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install health-data-analyzer
安装完成后，直接呼叫该 Skill 的名称或使用 /health-data-analyzer 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

- Initial release of health-data-analyzer, a professional tool for multi-dimensional health data analysis via the MCP server. - Supports sleep, exercise, and recovery data queries and analysis, including personalized health trends and recommendations. - Provides clear workflow and command-line steps for data access (list tables, get schema, query data). - Offers detailed table overview, analysis workflows, data quality checks, and output formats. - Includes troubleshooting guidance for common issues.

元数据

Slug health-data-analyzer

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题