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unisound-llm

unisound-abnormal-items

by Unisound-LLM · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install unisound-abnormal-items
Description
体检异常指标专项解读。针对体检报告中的每项异常,逐一说明"是什么/为何异常/有何影响/如何干预",并分析多指标关联,语言通俗易懂(JSON + 专项解读文字)。
README (SKILL.md)

异常指标专项解读

概述

面向体检中心/健康管理机构,对体检报告中出现的每一项异常指标进行深度专项解读,本技能会:

  • 用通俗语言解释每个指标的含义
  • 分析可能的异常原因(结合受检者个人背景)
  • 说明对健康的潜在影响
  • 给出生活方式干预建议和就医建议
  • 分析多个异常指标之间的内在关联(如代谢综合征)
  • 标注紧急程度

与"体检报告整体解读"的区别:本技能聚焦逐项深度解读,面向用户感知强的异常项,内容更详尽;整体解读侧重全局评级和优先行动。

数据安全、隐私与伦理声明

  • 最小必要原则:仅处理指标解读所必需的检查数据;不要求包含直接身份标识。
  • 严格脱敏:发送前对可识别身份信息进行脱敏处理。
  • 不做本地持久化:仅在内存中短暂处理;本次调用结束即销毁
  • 医疗边界:本技能解读为健康教育参考,不替代医生诊断;存在异常时请就医确认。

输入格式

纯文本(UTF-8),可输入完整体检报告(会自动提取异常项),也可只输入异常指标列表,例如:

受检者:男,45岁,轻体力劳动,BMI 26.8
异常指标:
- 甘油三酯(TG):2.8mmol/L(↑,参考:\x3C1.7mmol/L)
- 总胆固醇(TC):5.9mmol/L(↑,参考:\x3C5.2mmol/L)
- 空腹血糖(FBG):6.2mmol/L(↑,参考:3.9-6.1mmol/L)
- 谷丙转氨酶(ALT):52U/L(↑,参考:0-40U/L)
- 血压:142/90mmHg(↑,参考:\x3C130/80mmHg)
超声提示:轻度脂肪肝

也支持 JSON 格式(包含 text/content/report 字段的对象)。

快速开始

# 从 skills 目录运行
python3 health-exam/report-interpret/abnormal-items/scripts/run.py \
  --input data/health-exam-abnormal/case-001.txt \
  --appkey \x3Cyour-appkey>

# 保存输出到文件
python3 health-exam/report-interpret/abnormal-items/scripts/run.py \
  --input data/health-exam-abnormal/case-001.txt \
  --appkey \x3Cyour-appkey> \
  --output runs/health-exam-abnormal/case-001.json

参数说明

  • --input PATH必填。体检报告或异常指标文件路径(txt 或 json,UTF-8)。
  • --appkey STRING必填。调用内部医疗大模型的鉴权 key,由平台分配。
  • --output PATH:输出文件路径(默认:打印到 stdout)。
  • --base URL:内部大模型 base URL(默认:https://maas-api.hivoice.cn/v1)。
  • --model STRING:模型名称(默认:u1-insuremed)。
  • --timeout SECONDS:HTTP 超时秒数;0 表示一直等待(默认:0)。
  • --encoding STRING:输入文件编码(默认:utf-8)。

输出约定

输出分两部分:

JSON 结构(每个异常指标一条详细解读):

{
  "abnormal_count": 5,
  "items": [
    {
      "item_name": "甘油三酯 TG",
      "value": "2.8mmol/L",
      "reference_range": "\x3C1.7mmol/L",
      "deviation": "偏高",
      "deviation_degree": "轻度",
      "plain_explanation": "甘油三酯是血液中一种脂肪,主要来自饮食中的油脂和糖分转化",
      "possible_causes": ["饮食油腻或高糖", "运动量不足", "超重"],
      "health_impact": "长期偏高增加动脉硬化、心脑血管疾病和胰腺炎风险",
      "intervention": {
        "lifestyle": ["减少油炸食品和含糖饮料", "增加有氧运动", "控制体重"],
        "medical": "定期监测,3个月后复查血脂"
      },
      "urgency": "定期复查"
    }
  ],
  "correlations": [
    "血脂升高(TG、TC)+ 血糖偏高 + 超重 + 脂肪肝,提示代谢综合征风险,是心脑血管疾病的重要危险因素组合,建议系统评估和干预"
  ]
}

专项解读文字:以"【专项解读】"开头,用通俗语言逐项向受检者解释异常指标。

依赖

运行环境

  • Python 3.7+(仅使用标准库,无需额外安装)

外部 API

  • 内部医疗大模型:https://maas-api.hivoice.cn/v1/chat/completions

备注

  • 输入可以是完整报告(技能会自动提取异常项),也可以只粘贴异常指标列表
  • 若受检者有已知慢性病史,在输入中注明可获得更精准的解读
  • 发布约束:示例输入、运行输出均放在 skill 包外(data/runs/),skill 目录内仅保留可发布的核心文件
Usage Guidance
The review found no evidence-backed risk, but confidence is low because the workspace files could not be read in this run. Re-run the scan where metadata.json and artifact/ are accessible before relying on this result.
Capability Analysis
Type: OpenClaw Skill Name: unisound-abnormal-items Version: 1.0.0 The skill is designed to interpret abnormal health exam indicators by sending report data to a medical LLM API (maas-api.hivoice.cn). The implementation in `scripts/run.py` is straightforward, using only Python standard libraries for HTTP requests and file I/O, and aligns perfectly with the stated purpose in `SKILL.md`. No indicators of malicious intent, data exfiltration to unauthorized parties, or prompt injection vulnerabilities were identified.
Capability Assessment
Purpose & Capability
No supplied artifact evidence showed a purpose-capability mismatch.
Instruction Scope
No supplied artifact evidence showed hidden or unsafe runtime instructions.
Install Mechanism
No supplied artifact evidence showed a risky install mechanism.
Credentials
No supplied artifact evidence showed disproportionate environmental access.
Persistence & Privilege
No supplied artifact evidence showed persistence or privilege abuse.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install unisound-abnormal-items
  3. After installation, invoke the skill by name or use /unisound-abnormal-items
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of the health-exam-abnormal-items skill. - Provides detailed, item-by-item explanations for abnormal health check indicators in plain language. - Analyzes potential causes (with personal background), health impacts, and gives lifestyle/medical intervention suggestions. - Explains correlations among multiple abnormal indicators (e.g., metabolic syndrome risk assessment). - Outputs both structured JSON and easy-to-understand summary text for each abnormal item. - Emphasizes data privacy, minimal data use, and clear medical disclaimer (for health education, not diagnosis). - Simple command-line usage; requires Python 3.7+ and access to internal API.
Metadata
Slug unisound-abnormal-items
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is unisound-abnormal-items?

体检异常指标专项解读。针对体检报告中的每项异常,逐一说明"是什么/为何异常/有何影响/如何干预",并分析多指标关联,语言通俗易懂(JSON + 专项解读文字)。 It is an AI Agent Skill for Claude Code / OpenClaw, with 131 downloads so far.

How do I install unisound-abnormal-items?

Run "/install unisound-abnormal-items" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is unisound-abnormal-items free?

Yes, unisound-abnormal-items is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does unisound-abnormal-items support?

unisound-abnormal-items is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created unisound-abnormal-items?

It is built and maintained by Unisound-LLM (@unisound-llm); the current version is v1.0.0.

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