{"id":"6bfddfaa-d80c-410a-bc41-9d4ea3655797","entityType":"agent","slug":"clawhub-unisound-llm-unisound-abnormal-indicator-alert","name":"unisound-abnormal-indicator-alert","canonicalUrl":"https://www.xpersona.co/agent/clawhub-unisound-llm-unisound-abnormal-indicator-alert","canonicalPath":"/agent/clawhub-unisound-llm-unisound-abnormal-indicator-alert","generatedAt":"2026-10-12T11:26:15.980Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-11T22:04:20.936Z","emptyReason":null},"description":"病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。 Skill: unisound-abnormal-indicator-alert Owner: unisound-llm Summary: 病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T07:53:38.763Z | user - Removed the file skill-card.md. - Updated underlying medical model in the SKILL.md from u1-insuremed to u2-med. v1.0.0 | 2026-05-18T02:06:20.067Z | user - Initial release of the skill, renamed to *","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 987 downloads reported by the source. 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no default thresholds are built-in.\n\nArchive index:\n\nArchive v1.0.2: 5 files, 11307 bytes\n\nFiles: _meta.json (152b), scripts/preprocess.py (8744b), scripts/run.py (11055b), skill-card.md (2484b), SKILL.md (5015b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: med-patient-abnormal-indicator-alert\ndescription: 病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。\nmetadata:\n {\n \"openclaw\":\n {\n \"emoji\": \"⚠️\"\n }\n }\n---\n\n# 异常指标预警\n\n概述\n----\n本 skill 对应：病人端 / 慢病管理 / 异常指标预警。\n\n要求：慢病闭环能力。\n\n来源核验\n--------\n- 匹配来源：Open Wearables\n- 来源类型：公开开源健康数据平台\n- 来源链接：https://openwearables.io/\n- 匹配结论：部分匹配且可参考。Open Wearables 明确包含 health scoring、trends、anomalies、MCP 和可调阈值思路；本 skill 只借鉴异常识别结构，不照搬其可穿戴设备指标体系。\n\n参考部分\n--------\n只参考 Open Wearables 的 **trends/anomalies reasoning** 部分：\n- 指标阈值\n- 趋势识别\n- 异常识别\n- 风险提示\n- 可解释建议\n\n不参考部分\n----------\n- 不参考可穿戴设备接入\n- 不参考睡眠、恢复、运动负荷等非慢病指标\n- 不参考完整平台部署\n- 不扩展为临床诊断系统\n\n构建方式\n--------\nOpenClaw 中应构建为一个独立的预警型 skill：\n- 输入血糖、血压、心率等慢病指标记录\n- 根据配置阈值判断是否异常\n- 输出异常等级、异常原因和建议动作\n- 对连续异常或明显超阈值记录给出复诊/随访提示\n\n建议输入字段\n------------\n- `indicator_type`：指标类型，如血糖、血压、心率\n- `value`：指标值或指标对象\n- `unit`\n- `measured_at`\n- `threshold_profile`：阈值配置\n\n建议输出字段\n------------\n- `skill`：`异常指标预警`\n- `indicator_type`\n- `is_abnormal`\n- `alert_level`\n- `reason`\n- `suggested_action`\n\n医疗边界\n--------\n本 skill 只做基于规则的异常提示，不做诊断，不给出治疗方案。\n\n快速开始\n--------\n从本 skill 目录执行：\n\n```bash\npython3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY\n```\n\n最小输入示例\n------------\n```json\n{\n \"indicator_type\": \"blood_glucose\",\n \"value\": 11.2,\n \"unit\": \"mmol/L\",\n \"measured_at\": \"2026-04-29 08:00\",\n \"threshold_profile\": {\n \"high\": 10.0\n },\n \"suggested_action\": \"按既定随访规则提醒用户关注\"\n}\n```\n\n说明\n----\n本 skill 不内置医学阈值；所有阈值必须由调用方通过 `threshold_profile` 提供。\n\n输出约定\n--------\n输出 UTF-8 JSON，采用统一格式：\n\n```json\n{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}\n```\n\n- `data`：本地预处理得到的结构化数据\n- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式\n\n支持的输入格式\n--------------\n除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：\n\n| 格式 | 说明 |\n|------|------|\n| JSON | 默认，直接读取结构化输入 |\n| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |\n| TXT / MD | key:value 文本格式（支持中文/英文字段名） |\n| PDF / DOC / DOCX | 文档，提取文本后解析 |\n| PNG / JPG 等图片 | OCR 提取文本后解析 |\n\n文本格式示例\n-----------\n```\n指标类型：blood_glucose\n值：11.2\n单位：mmol/L\n```\n\nCSV 格式示例\n-----------\n```\n指标类型,值,单位\nblood_glucose,11.2,mmol/L\n```\n\n统一入口附加参数\n----------------\n- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。\n- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。\n- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。\n- `--save-prepared`：保存预处理后的 JSON，便于调试。\n- `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。\n\n依赖\n----\n### 运行环境\n- Python 3.7+\n\n### Python 第三方包（可选，按输入格式需要）\n| 包名 | 用途 | 必要条件 |\n|------|------|---------|\n| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |\n| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |\n\n### 外部工具（可选，按输入格式需要）\n| 工具 | 用途 | 必要条件 |\n|------|------|---------|\n| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |\n| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |\n| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |\n\n> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。\n\n模型配置\n--------\n本 skill 执行时通过内部医疗大模型进行推理：\n\n- endpoint：`https://maas-api.hivoice.cn/v1/chat/completions`\n- model：`u2-med`\n- 协议：OpenAI Chat Completions（兼容标准 /v1/chat/completions）\n- 鉴权：通过 `--appkey` 参数传入 Bearer token，由用户在 OpenClaw 中调用时提供\n\n> 本 skill 强制走 API 推理，无本地透传模式。\n\nFile v1.0.2:_meta.json\n\n{\n \"ownerId\": \"kn7ft98qwhxyrgcy7b7rzdhrrd8366k6\",\n \"slug\": \"unisound-abnormal-indicator-alert\",\n \"version\": \"1.0.2\",\n \"publishedAt\": 1784188418763\n}\n\nFile v1.0.2:skill-card.md\n\n## Description:\n\nAlerts chronic-care users to abnormal patient indicators by checking caller-provided thresholds and generating explanatory follow-up guidance.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[unisound-llm](https://clawhub.ai/user/unisound-llm)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nPatients, care teams, and developers can use this skill to process chronic-disease indicator records, flag values outside configured thresholds, and produce a structured alert plus patient-facing guidance. It is an alerting aid, not a diagnostic or treatment-planning system.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Sensitive medical indicator data is sent to an external model service.\n\nMitigation: Use the skill only where users are allowed to send this data to the listed provider; require consent and minimize or redact inputs for patient-facing use.\n\nRisk: The fallback shared preprocessor can run unreviewed local code.\n\nMitigation: Remove the fallback or pin it to trusted, reviewed code before installation or deployment.\n\nRisk: Local document and OCR processing can expand the file-processing attack surface.\n\nMitigation: Run document and OCR tools in a sandbox with restricted file access.\n\nRisk: Generated medical text may be incorrect or exceed the skill's alerting boundary.\n\nMitigation: Validate generated medical text against approved rules and keep use limited to alerts rather than diagnosis or treatment planning.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/unisound-llm/skills/unisound-abnormal-indicator-alert)\n- [Open Wearables](https://openwearables.io/)\n- [Hivoice medical chat completions endpoint](https://maas-api.hivoice.cn/v1/chat/completions)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, JSON, Guidance]\n\n**Output Format:** [UTF-8 JSON containing structured alert data and Markdown natural-language analysis]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires caller-provided thresholds and an appkey; the skill does not include built-in medical thresholds.]\n\n## Skill Version(s):\n\n1.0.2 (source: server release metadata; artifact _meta.json says 1.0.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.0: 5 files, 11402 bytes\n\nFiles: _meta.json (152b), scripts/preprocess.py (8744b), scripts/run.py (11061b), skill-card.md (2779b), SKILL.md (5021b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: med-patient-abnormal-indicator-alert\ndescription: 病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。\nmetadata:\n {\n \"openclaw\":\n {\n \"emoji\": \"⚠️\"\n }\n }\n---\n\n# 异常指标预警\n\n概述\n----\n本 skill 对应：病人端 / 慢病管理 / 异常指标预警。\n\n要求：慢病闭环能力。\n\n来源核验\n--------\n- 匹配来源：Open Wearables\n- 来源类型：公开开源健康数据平台\n- 来源链接：https://openwearables.io/\n- 匹配结论：部分匹配且可参考。Open Wearables 明确包含 health scoring、trends、anomalies、MCP 和可调阈值思路；本 skill 只借鉴异常识别结构，不照搬其可穿戴设备指标体系。\n\n参考部分\n--------\n只参考 Open Wearables 的 **trends/anomalies reasoning** 部分：\n- 指标阈值\n- 趋势识别\n- 异常识别\n- 风险提示\n- 可解释建议\n\n不参考部分\n----------\n- 不参考可穿戴设备接入\n- 不参考睡眠、恢复、运动负荷等非慢病指标\n- 不参考完整平台部署\n- 不扩展为临床诊断系统\n\n构建方式\n--------\nOpenClaw 中应构建为一个独立的预警型 skill：\n- 输入血糖、血压、心率等慢病指标记录\n- 根据配置阈值判断是否异常\n- 输出异常等级、异常原因和建议动作\n- 对连续异常或明显超阈值记录给出复诊/随访提示\n\n建议输入字段\n------------\n- `indicator_type`：指标类型，如血糖、血压、心率\n- `value`：指标值或指标对象\n- `unit`\n- `measured_at`\n- `threshold_profile`：阈值配置\n\n建议输出字段\n------------\n- `skill`：`异常指标预警`\n- `indicator_type`\n- `is_abnormal`\n- `alert_level`\n- `reason`\n- `suggested_action`\n\n医疗边界\n--------\n本 skill 只做基于规则的异常提示，不做诊断，不给出治疗方案。\n\n快速开始\n--------\n从本 skill 目录执行：\n\n```bash\npython3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY\n```\n\n最小输入示例\n------------\n```json\n{\n \"indicator_type\": \"blood_glucose\",\n \"value\": 11.2,\n \"unit\": \"mmol/L\",\n \"measured_at\": \"2026-04-29 08:00\",\n \"threshold_profile\": {\n \"high\": 10.0\n },\n \"suggested_action\": \"按既定随访规则提醒用户关注\"\n}\n```\n\n说明\n----\n本 skill 不内置医学阈值；所有阈值必须由调用方通过 `threshold_profile` 提供。\n\n输出约定\n--------\n输出 UTF-8 JSON，采用统一格式：\n\n```json\n{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}\n```\n\n- `data`：本地预处理得到的结构化数据\n- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式\n\n支持的输入格式\n--------------\n除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：\n\n| 格式 | 说明 |\n|------|------|\n| JSON | 默认，直接读取结构化输入 |\n| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |\n| TXT / MD | key:value 文本格式（支持中文/英文字段名） |\n| PDF / DOC / DOCX | 文档，提取文本后解析 |\n| PNG / JPG 等图片 | OCR 提取文本后解析 |\n\n文本格式示例\n-----------\n```\n指标类型：blood_glucose\n值：11.2\n单位：mmol/L\n```\n\nCSV 格式示例\n-----------\n```\n指标类型,值,单位\nblood_glucose,11.2,mmol/L\n```\n\n统一入口附加参数\n----------------\n- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。\n- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。\n- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。\n- `--save-prepared`：保存预处理后的 JSON，便于调试。\n- `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。\n\n依赖\n----\n### 运行环境\n- Python 3.7+\n\n### Python 第三方包（可选，按输入格式需要）\n| 包名 | 用途 | 必要条件 |\n|------|------|---------|\n| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |\n| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |\n\n### 外部工具（可选，按输入格式需要）\n| 工具 | 用途 | 必要条件 |\n|------|------|---------|\n| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |\n| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |\n| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |\n\n> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。\n\n模型配置\n--------\n本 skill 执行时通过内部医疗大模型进行推理：\n\n- endpoint：`https://maas-api.hivoice.cn/v1/chat/completions`\n- model：`u1-insuremed`\n- 协议：OpenAI Chat Completions（兼容标准 /v1/chat/completions）\n- 鉴权：通过 `--appkey` 参数传入 Bearer token，由用户在 OpenClaw 中调用时提供\n\n> 本 skill 强制走 API 推理，无本地透传模式。\n\nFile v1.0.0:_meta.json\n\n{\n \"ownerId\": \"kn7ft98qwhxyrgcy7b7rzdhrrd8366k6\",\n \"slug\": \"unisound-abnormal-indicator-alert\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1779069980067\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:
\nAlerts on abnormal chronic-disease indicators such as glucose, blood pressure, and heart rate using caller-supplied thresholds and remote medical model analysis.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[unisound-llm](https://clawhub.ai/user/unisound-llm)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nHealth application and care-management developers use this skill to flag patient-side chronic-disease measurements against supplied thresholds and generate an explanatory alert. The output is for monitoring and follow-up support, not diagnosis or treatment.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Sensitive health indicators and related context are sent to a remote medical model service.
\nMitigation: Use the skill only when users and administrators accept that data flow, minimize submitted context, and apply appropriate privacy controls.
\nRisk: The app key is a sensitive credential required for remote model access.
\nMitigation: Store and pass the key through a secure secret mechanism and keep it out of command history, process logs, and committed files.
\nRisk: Untrusted PDF, DOC, XLS, or image inputs may exercise broad document conversion and OCR paths.
\nMitigation: Prefer structured JSON or CSV inputs; run document conversion and OCR in a sandbox when handling untrusted files.
\nRisk: Generated medical explanations could be mistaken for diagnosis or treatment advice.
\nMitigation: Present results as threshold-based monitoring alerts and require qualified clinical review for medical decisions.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/unisound-llm/unisound-abnormal-indicator-alert)
\n- [Unisound-LLM publisher profile](https://clawhub.ai/user/unisound-llm)
\n- [Open Wearables](https://openwearables.io/)
\n- [Hivoice MaaS chat completions endpoint](https://maas-api.hivoice.cn/v1/chat/completions)
\n\n\n## Skill Output:
\n**Output Type(s):** [json, markdown, text, guidance]
\n**Output Format:** [UTF-8 JSON containing structured alert fields and Markdown explanatory text]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Includes abnormality status, alert level, rule-based reasons, and model-generated explanatory guidance.]
\n\n## Skill Version(s):
\n1.0.0 (source: server release evidence and artifact _meta.json)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
","readmeExcerpt":"Skill: unisound-abnormal-indicator-alert Owner: unisound-llm Summary: 病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T07:53:38.763Z | user - Removed the file skill-card.md. - Updated underlying medical model in the SKILL.md from u1-insuremed to u2-med. v1.0.0 | 2026-05-18T02:06:20.067Z | user - Initial release of the skill, renamed to *","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"python3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY"},{"language":"json","snippet":"{\n \"indicator_type\": \"blood_glucose\",\n \"value\": 11.2,\n \"unit\": \"mmol/L\",\n \"measured_at\": \"2026-04-29 08:00\",\n \"threshold_profile\": {\n \"high\": 10.0\n },\n \"suggested_action\": \"按既定随访规则提醒用户关注\"\n}"},{"language":"json","snippet":"{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}"},{"language":"text","snippet":"指标类型：blood_glucose\n值：11.2\n单位：mmol/L"},{"language":"text","snippet":"指标类型,值,单位\nblood_glucose,11.2,mmol/L"},{"language":"bash","snippet":"python3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: med-patient-abnormal-indicator-alert\ndescription: 病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。\nmetadata:\n {\n \"openclaw\":\n {\n \"emoji\": \"⚠️\"\n }\n }\n---\n\n# 异常指标预警\n\n概述\n----\n本 skill 对应：病人端 / 慢病管理 / 异常指标预警。\n\n要求：慢病闭环能力。\n\n来源核验\n--------\n- 匹配来源：Open Wearables\n- 来源类型：公开开源健康数据平台\n- 来源链接：https://openwearables.io/\n- 匹配结论：部分匹配且可参考。Open Wearables 明确包含 health scoring、trends、anomalies、MCP 和可调阈值思路；本 skill 只借鉴异常识别结构，不照搬其可穿戴设备指标体系。\n\n参考部分\n--------\n只参考 Open Wearables 的 **trends/anomalies reasoning** 部分：\n- 指标阈值\n- 趋势识别\n- 异常识别\n- 风险提示\n- 可解释建议\n\n不参考部分\n----------\n- 不参考可穿戴设备接入\n- 不参考睡眠、恢复、运动负荷等非慢病指标\n- 不参考完整平台部署\n- 不扩展为临床诊断系统\n\n构建方式\n--------\nOpenClaw 中应构建为一个独立的预警型 skill：\n- 输入血糖、血压、心率等慢病指标记录\n- 根据配置阈值判断是否异常\n- 输出异常等级、异常原因和建议动作\n- 对连续异常或明显超阈值记录给出复诊/随访提示\n\n建议输入字段\n------------\n- `indicator_type`：指标类型，如血糖、血压、心率\n- `value`：指标值或指标对象\n- `unit`\n- `measured_at`\n- `threshold_profile`：阈值配置\n\n建议输出字段\n------------\n- `skill`：`异常指标预警`\n- `indicator_type`\n- `is_abnormal`\n- `alert_level`\n- `reason`\n- `suggested_action`\n\n医疗边界\n--------\n本 skill 只做基于规则的异常提示，不做诊断，不给出治疗方案。\n\n快速开始\n--------\n从本 skill 目录执行：\n\n```bash\npython3 scripts/run.py --input input.json --output output.json --appkey YOUR_KEY\n```\n\n最小输入示例\n------------\n```json\n{\n \"indicator_type\": \"blood_glucose\",\n \"value\": 11.2,\n \"unit\": \"mmol/L\",\n \"measured_at\": \"2026-04-29 08:00\",\n \"threshold_profile\": {\n \"high\": 10.0\n },\n \"suggested_action\": \"按既定随访规则提醒用户关注\"\n}\n```\n\n说明\n----\n本 skill 不内置医学阈值；所有阈值必须由调用方通过 `threshold_profile` 提供。\n\n输出约定\n--------\n输出 UTF-8 JSON，采用统一格式：\n\n```json\n{\n \"skill\": \"技能名称\",\n \"status\": \"ok\",\n \"data\": { /* 结构化数据 */ },\n \"text\": \"API 生成的 Markdown/自然语言内容，OpenClaw 直接渲染给用户\"\n}\n```\n\n- `data`：本地预处理得到的结构化数据\n- `text`：内部医疗大模型生成的自然语言解读/分析/提醒，Markdown 格式\n\n支持的输入格式\n--------------\n除 JSON 外，还支持以下格式（通过 `--input-type` 自动检测或手动指定）：\n\n| 格式 | 说明 |\n|------|------|\n| JSON | 默认，直接读取结构化输入 |\n| CSV / XLSX / XLS | 表格数据，按列头自动映射字段 |\n| TXT / MD | key:value 文本格式（支持中文/英文字段名） |\n| PDF / DOC / DOCX | 文档，提取文本后解析 |\n| PNG / JPG 等图片 | OCR 提取文本后解析 |\n\n文本格式示例\n-----------\n```\n指标类型：blood_glucose\n值：11.2\n单位：mmol/L\n```\n\nCSV 格式示例\n-----------\n```\n指标类型,值,单位\nblood_glucose,11.2,mmol/L\n```\n\n统一入口附加参数\n----------------\n- `--input-type auto|pdf|doc|docx|xls|xlsx|csv|txt|json`：输入类型；默认 `auto`。\n- `--sheet STRING`：读取 Excel 时指定 sheet（可选）。\n- `--encoding STRING`：`txt/csv` 编码（默认：`utf-8`）。\n- `--save-prepared`：保存预处理后的 JSON，便于调试。\n- `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。\n\n依赖\n----\n### 运行环境\n- Python 3.7+\n\n### Python 第三方包（可选，按输入格式需要）\n| 包名 | 用途 | 必要条件 |\n|------|------|---------|\n| `openpyxl` | 读取 `.xlsx` 文件 | 输入为 xlsx 时必须 |\n| `pypdf` | 提取 PDF 文本 | 输入为 pdf 时必须 |\n\n### 外部工具（可选，按输入格式需要）\n| 工具 | 用途 | 必要条件 |\n|------|------|---------|\n| LibreOffice (`soffice`) | 转换 `.doc` / `.xls` | 输入为 doc/xls 时必须 |\n| `pdftotext`（poppler-utils） | 提取 PDF 文本 | 输入为 pdf 且未安装 pypdf 时 |\n| `tesseract`（含 chi_sim+eng） | 图片 OCR | 输入为图片时必须 |\n\n> 仅使用 JSON 输入时，无需安装任何第三方包或外部工具。\n\n模型配置\n--------\n本 skill 执行时通过内部医疗大模型进行推理：\n\n- endpoint：`https://maas-api.hivoice.cn/v1/chat/completions`\n- m"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn7ft98qwhxyrgcy7b7rzdhrrd8366k6\",\n \"slug\": \"unisound-abnormal-indicator-alert\",\n \"version\": \"1.0.2\",\n \"publishedAt\": 1784188418763\n}"},{"path":"skill-card.md","content":"## Description:\n\nAlerts chronic-care users to abnormal patient indicators by checking caller-provided thresholds and generating explanatory follow-up guidance.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[unisound-llm](https://clawhub.ai/user/unisound-llm)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nPatients, care teams, and developers can use this skill to process chronic-disease indicator records, flag values outside configured thresholds, and produce a structured alert plus patient-facing guidance. It is an alerting aid, not a diagnostic or treatment-planning system.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Sensitive medical indicator data is sent to an external model service.\n\nMitigation: Use the skill only where users are allowed to send this data to the listed provider; require consent and minimize or redact inputs for patient-facing use.\n\nRisk: The fallback shared preprocessor can run unreviewed local code.\n\nMitigation: Remove the fallback or pin it to trusted, reviewed code before installation or deployment.\n\nRisk: Local document and OCR processing can expand the file-processing attack surface.\n\nMitigation: Run document and OCR tools in a sandbox with restricted file access.\n\nRisk: Generated medical text may be incorrect or exceed the skill's alerting boundary.\n\nMitigation: Validate generated medical text against approved rules and keep use limited to alerts rather than diagnosis or treatment planning.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/unisound-llm/skills/unisound-abnormal-indicator-alert)\n- [Open Wearables](https://openwearables.io/)\n- [Hivoice medical chat completions endpoint](https://maas-api.hivoice.cn/v1/chat/completions)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, JSON, Guidance]\n\n**Output Format:** [UTF-8 JSON containing structured alert data and Markdown natural-language analysis]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Requires caller-provided thresholds and an appkey; the skill does not include built-in medical thresholds.]\n\n## Skill Version(s):\n\n1.0.2 (source: server release metadata; artifact _meta.json says 1.0.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。 Skill: unisound-abnormal-indicator-alert Owner: unisound-llm Summary: 病人端慢病管理异常指标预警。参考 Open Wearables 的 trends/anomalies reasoning 部分，构建慢病闭环能力。 Tags: latest:1.0.2 Version history: v1.0.2 | 2026-07-16T07:53:38.763Z | user - Removed the file skill-card.md. - Updated underlying medical model in the SKILL.md from u1-insuremed to u2-med. v1.0.0 | 2026-05-18T02:06:20.067Z | user - Initial release of the skill, renamed to *","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":959,"uniquenessScore":54,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-11T22:04:20.936Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-11T22:04:20.936Z","emptyReason":"This page has not been claimed by the agent owner."},"hasCustomPage":false,"customPageUpdatedAt":null,"customLinks":[],"structuredLinks":{"docsUrl":null,"demoUrl":null,"supportUrl":null,"pricingUrl":null,"statusUrl":null},"customPage":null},"relatedAgents":{"evidence":{"source":"protocol-neighbors","verified":false,"confidence":"medium","updatedAt":"2026-10-12T11:26:15.980Z","emptyReason":null},"items":[{"id":"8ebccd8e-3863-4187-8355-c3f14e1f9edf","entityType":"agent","canonicalPath":"/agent/iofficeai-aionui","slug":"iofficeai-aionui","name":"AionUi","description":"Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!","url":"https://github.com/iOfficeAI/AionUi","homepage":"https://www.aionui.com","source":"GITHUB_REPOS","protocols":["MCP","OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-10-09T19:11:12.944Z","createdAt":"2026-02-25T03:38:16.584Z","downloads":null},{"id":"b917f68a-ebff-438e-84f8-3f4b2494c0bc","entityType":"agent","canonicalPath":"/agent/activepieces-activepieces","slug":"activepieces-activepieces","name":"activepieces","description":"AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents","url":"https://github.com/activepieces/activepieces","homepage":"https://www.activepieces.com","source":"GITHUB_REPOS","protocols":["OPENCLAW"],"capabilities":[],"safetyScore":100,"overallRank":70,"updatedAt":"2026-04-15T02:22:12.426Z","createdAt":"2026-02-25T03:38:12.412Z","downloads":null},{"id":"5cb26759-3a39-483f-94cf-276a98c13bb8","entityType":"agent","canonicalPath":"/agent/cherryhq-cherry-studio","slug":"cherryhq-cherry-studio","name":"cherry-studio","description":"AI productivity studio with smart chat, autonomous agents, and 300+ assistants. 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