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Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具

Based on computer vision, automatically detects coughing, phlegm, and wheezing frequency, counts the frequency of episodes, used for early health anomaly alerts, helping to detect respiratory diseases in a timely manner. | 呼吸道症状智能识别技能，基于计算机视觉自动检测咳嗽、咳痰、喘息频率，统计发作频次，用于健康异常早期提醒，帮助及时发现呼吸道疾病 Skill: Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具 Owner: 18072937735 Summary: Based on computer vision, automatically detects coughing, phlegm, and wheezing frequency, counts the frequency of episodes, used for early health anomaly alerts, helping to detect respiratory diseases in a timely manner. | 呼吸道症状智能识别技能，基于计算机视觉自动检测咳嗽、咳痰、喘息频率，统计发作频次，用于健康异常早期提醒，帮助及时发现呼吸道疾病 Tags: latest:1.0.18 Version history: v1.0

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

2.5k

Updated

Oct 9, 2026

Version

1.0.18

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.5K downloads reported by the source. Last updated 10/9/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 9, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 9, 2026
Adoption signal
2.5K downloadsadoption · observed Oct 9, 2026
Latest release
1.0.18release · observed Sep 30, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-respiratory-symptom-recognition-analysis
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-18072937735-smyx-respiratory-symptom-recognition-analys/snapshot"

Documentation

CLAWHUB

144,077 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "respiratory_symptom_recognition_analysis"
description: "Based on computer vision, automatically detects coughing, phlegm, and wheezing frequency, counts the frequency of episodes, used for early health anomaly alerts, helping to detect respiratory diseases in a timely manner. | 呼吸道症状智能识别技能，基于计算机视觉自动检测咳嗽、咳痰、喘息频率，统计发作频次，用于健康异常早期提醒，帮助及时发现呼吸道疾病"
version: "1.0.19"
license: "MIT-0"
---

# 🫁 Respiratory Symptom Smart Recognition Tool | 呼吸道症状智能识别工具
> **智能分析中枢** · 图片/视频智能分析 · 结构化报告 · 历史报告云端查询

---

## 🧭 技能概览 | Overview

| 模块 | 内容 |
|---|---|
| 🏷️ 技能名称 | **呼吸道症状智能识别工具** |
| 🎯 核心目标 | 呼吸道症状智能识别技能，基于计算机视觉自动检测咳嗽、咳痰、喘息频率，统计发作频次，用于健康异常早期提醒，帮助及时发现呼吸道疾病 |
| 🖼️ 输入类型 | 图片、视频、本地文件、网络 URL |
| 📝 输出能力 | 结构化分析报告、识别/监测结果、建议与报告链接 |
| 🧩 场景码 | `RESPIRATORY_SYMPTOM_RECOGNITION` |

Based on advanced computer vision and behavior recognition algorithms, this feature automatically detects and counts the
frequency of respiratory symptoms such as coughing, expectoration, and wheezing. Through real-time video analysis, the
system precisely captures key characteristics including chest movement, body posture, and mouth actions, effectively
distinguishing between normal breathing and abnormal symptomatic behaviors. Additionally, the system automatically logs
the time, frequency, and duration of symptom episodes to generate dynamic health trend charts. When the frequency of
symptoms exceeds normal thresholds, it promptly issues health anomaly alerts, helping users and their families detect
signs of respiratory disease early and providing data support for timely medical consultation.

本功能基于先进的计算机视觉与行为识别算法，能够自动检测并统计用户的咳嗽、咳痰及喘息等呼吸道症状的发作频率。系统通过实时视频分析，精准捕捉胸部起伏、身体姿态及口部动作等关键特征，有效区分正常呼吸与异常症状行为。同时，系统会自动记录症状发作的时间、频次及持续时长，生成动态健康趋势图，当检测到症状频次超出正常阈值时，及时发出健康异常提醒，帮助用户及家属早期发现呼吸道疾病迹象，为及时就医提供数据支持

## 🎬 技能演示 | Skill Demo

[▶️ 点击查看技能使用介绍](https://lifeemergence.com/sample.html)

---

## 🎯 任务目标 | Goals

### 1. 🧩 技能用途

通过视频进行呼吸道症状智能识别，自动检测咳嗽、咳痰、喘息等症状，统计发作频率，生成健康监测报告，实现早期异常提醒

### 2. 🛠️ 能力范围

| 序号 | 具体能力 |
|---:|---|
| 1 | 视频分析 |
| 2 | 咳嗽动作识别 |
| 3 | 咳痰识别 |
| 4 | 喘息识别 |
| 5 | 发作频次统计 |
| 6 | 症状严重程度评估 |
| 7 | 健康风险预警 |
| 8 | 就医建议生成 |

### 3. ⚡ 触发条件

| 触发类型 | 触发规则 |
|---|---|
| ✅ 默认触发 | **默认触发**：当用户提供视频 URL 或文件需要进行呼吸道症状识别时，默认触发本技能进行分析 |
| 🔎 明确分析意图 | 当用户明确需要进行呼吸道监测、咳嗽识别、症状统计，提及咳嗽、咳痰、喘息、呼吸道、肺部监测等关键词，并且上传了视频文件或者图片文件 |
| 📚 历史报告查询 | 当用户提及以下关键词时，**自动触发历史报告查询功能** ：查看历史监测报告、历史症状报告、呼吸道识别报告清单、查询历史报告、查看监测报告列表、显示所有监测报告、显示呼吸道分析报告，查询呼吸道症状识别报告 |

### 4. 🤖 自动行为

| 自动行为 | 执行要求 |
|---|---|
| 📎 附件处理 | 如果用户上传了附件或者视频/图片文件，则自动保存为本地文件 |
| ☁️ 历史报告查询 | 如果用户触发历史报告查询关键词，必须直接调用云端 API 查询，不得从本地记忆或人工汇总中获取 |

#### ⚠️ 强制数据获取规则（次高优先级）

> **橙色强约束：** 历史报告清单只允许从云端接口读取，不允许从本地记录、长期记忆或人工汇总中提取。

必须执行：

```bash
python -m scripts.respiratory_symptom_recognition_analysis --list
```

| 类型 | 要求 |
|---|---|
| ✅ 必须 | 使用 `python -m scripts.respiratory_symptom_recognition_analysis --list` 调用 API 查询云端的历史报告数据 |
| 🚫 严格禁止 | 从本地 `memory` 目录读取历史会话信息 |
| 🚫 严格禁止 | 手动汇总本地记录中的报告 |
| 🚫 严格禁止 | 从长期记忆中提取报告 |
| ✅ 输出格式 | 必须统一从云端接口获取最新完整数据，然后以 Markdown

_meta.json

{
  "ownerId": "kn7e2caqj7pnsvr9r7t8zenghs83xw7n",
  "slug": "smyx-respiratory-symptom-recognition-analysis",
  "version": "1.0.18",
  "publishedAt": 1790769280759
}

references/api_doc.md

# 呼吸道症状智能识别分析 API 文档

## 接口概述

本技能调用云端视觉AI接口，自动识别视频中的咳嗽、咳痰、喘息等呼吸道症状，统计发作频次，评估严重程度，实现健康异常早期提醒。

## 支持识别的呼吸道症状

| 症状类型 | 描述 | 统计方式 |
|----------|------|----------|
| 咳嗽 | 胸部收缩+张口咳嗽动作识别 | 按发作次数统计 |
| 咳痰 | 咳嗽后清痰动作识别 | 按发作次数统计 |
| 喘息 | 呼吸急促、张口喘息识别 | 按发作次数统计 |
| 胸闷 | 胸部不适表情识别 | 按持续时间评估 |

## 监测场景

| 场景类型 | 适用场景 |
|----------|----------|
| 日常监测 | 居家日常健康监测 |
| 术后康复 | 手术后呼吸道康复监测 |
| 病房监测 | 医院病房持续监测 |
| 其他 | 自定义监测场景 |

## 风险等级划分

| 等级 | 描述 | 建议 |
|------|------|------|
| 🟢 正常 | 症状发作频次在正常范围 | 继续日常监测 |
| 🟡 轻度 | 轻度症状，偶发 | 注意休息，观察变化 |
| 🟠 中度 | 中度症状，频发 | 建议就医检查 |
| 🔴 重度 | 重度症状，频繁发作 | 立即就医 |

## API 响应字段说明

### 基础信息

| 字段 | 类型 | 说明 |
|------|------|------|
| id | string | 分析记录ID |
| data.analysis_time | string | 分析时间 |
| data.person_detection.status | string | 对象检测状态 |
| data.person_detection.quality_score | int | 画面质量评分 0-100 |

### 诊断结果

| 字段 | 类型 | 说明 |
|------|------|------|
| data.diagnosis.risk_score | int | 整体风险评分 0-100 |
| data.diagnosis.risk_level | string | 风险等级：normal/mild/moderate/severe |
| data.diagnosis.total_cough_count | int | 咳嗽总次数 |
| data.diagnosis.total_sputum_count | int | 咳痰总次数 |
| data.diagnosis.total_wheeze_count | int | 喘息总次数 |
| data.diagnosis.average_freq_per_minute | float | 平均每分钟发作频次 |
| data.diagnosis.symptom_counts | object | 各症状详细计数 |
| data.diagnosis.severity_assessment | object | 各症状严重程度评估 |

### 警示与建议

| 字段 | 类型 | 说明 |
|------|------|------|
| data.health_warnings | array[string] | 健康风险警示信息列表 |
| data.medical_suggestions | array[string] | 就医护理建议列表 |

## 错误码说明

| 错误码 | 说明 |
|--------|------|
| 200 | 请求成功 |
| 400 | 请求参数错误 |
| 401 | API 鉴权失败 |
| 413 | 文件大小超出限制 |
| 415 | 不支持的文件格式 |
| 500 | 服务器内部错误 |
| 503 | 服务繁忙，请稍后重试 |
| COMMON_AI_ANALYSIS_TIMEOUT | AI分析超时，请稍后重试 |

## 医学提示

1. 本工具仅用于辅助健康监测和早期异常提醒
2. 不能替代专业医师诊断、胸部X光、CT等医学检查
3. 分析结果仅供参考，确诊请遵医嘱进行相关检查
4. 如果出现严重呼吸困难，请立即就医，不要依赖工具监测

skills/smyx_analysis/references/api_doc.md

# API接口文档

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 请求格式：支持文件上传
- 响应格式：JSON

## 错误码说明

| 错误码 | 说明       |
|-----|----------|
| 400 | 请求参数错误   |
| 401 | API密钥无效  |
| 403 | 权限不足     |
| 413 | 文件过大     |
| 415 | 不支持的文件格式 |
| 500 | 服务器内部错误  |

scripts/config.yaml

{}

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 10, 2026.

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