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Real-time Employee Absence Monitoring Skill | 人员离岗实时监测技能

Real-time monitoring of personnel on-duty status in specific areas based on computer vision and human pose estimation, automatically detects abnormal statuses such as leaving posts and absent from work, supports custom threshold settings, and triggers early warning immediately when abnormality is detected. | 人员离岗实时监测技能，基于计算机视觉与人体姿态估计算法，实时监测特定区域内人员的在岗状态，自动判断离岗、缺岗等异常状态，支持自定义判定阈值，异常发生立即触发预警，适用于工厂车间、监控室、服务窗口等岗位监管场景 Skill: Real-time Employee Absence Monitoring Skill | 人员离岗实时监测技能 Owner: 18072937735 Summary: Real-time monitoring of personnel on-duty status in specific areas based on computer vision and human pose estimation, automatically detects abnormal statuses such as leaving posts and absent from work, supports custom threshold settings, and triggers early warning immediately when abnormality is detected. | 人员离岗实时监测技能，基于计算机视觉

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

2.2k

Updated

Oct 9, 2026

Version

1.0.15

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.2K 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: 2.2K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.15release · observed Oct 3, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-staff-absence-detection-analysis

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.
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-staff-absence-detection-analysis/snapshot"

Documentation

CLAWHUB

149,972 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "staff-absence-detection-analysis"
description: "Real-time monitoring of personnel on-duty status in specific areas based on computer vision and human pose estimation, automatically detects abnormal statuses such as leaving posts and absent from work, supports custom threshold settings, and triggers early warning immediately when abnormality is detected. | 人员离岗实时监测技能，基于计算机视觉与人体姿态估计算法，实时监测特定区域内人员的在岗状态，自动判断离岗、缺岗等异常状态，支持自定义判定阈值，异常发生立即触发预警，适用于工厂车间、监控室、服务窗口等岗位监管场景"
version: "1.0.19"
license: "MIT-0"
---

# 👤 Staff Absence Detection Skill | 人员离岗实时监测技能
> **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 | **人员离岗实时监测技能** |
| 🎯 核心目标 | 人员离岗实时监测技能，基于计算机视觉与人体姿态估计算法，实时监测特定区域内人员的在岗状态，自动判断离岗、缺岗等异常状态，支持自定义判定阈值，异常发生立即触发预警，适用于工厂车间、监控室、服务窗口等岗位监管场景 |
| 🖼️ 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 | `PERSONNEL_LEAVE_POST_MONITORING` |

Tailored specifically for post management supervision scenarios in industrial production, security monitoring, service
windows and other work scenarios, this skill is equipped with a high-precision computer vision algorithm combined with
human pose estimation technology, which can accurately identify the presence, location and behavior characteristics of
personnel in the monitoring area, automatically distinguish between temporary departure and long-term absence based on
time-series behavior analysis, and supports user-defined judgment thresholds such as departure duration and area range.

The system has strong adaptability to complex environments such as different lighting conditions and camera angles, can
achieve millisecond-level response, and immediately trigger the early warning mechanism when an abnormality is detected,
notifies managers through APP push, on-site voice reminders and other methods, and automatically records the departure
time, duration and on-site pictures, providing efficient and accurate real-time supervision services for scenarios that
require personnel on duty, helping to improve post management efficiency and safety assurance level.

本技能专为工厂车间、安保监控室、服务窗口等岗位管理监管场景量身打造，搭载高精度计算机视觉算法结合人体姿态估计技术，能够精准识别监测区域内人员的存在、定位及行为特征，基于时序行为分析自动区分短暂离开与长时间离岗，支持用户自定义离岗时长、区域范围等判定阈值。

系统对不同光照条件、摄像机角度等复杂环境具备强大的适应能力，可实现毫秒级响应，异常发生时立即触发预警机制，通过APP推送、现场语音提醒等方式通知管理人员，并自动记录离岗时间、时长及现场画面，为需要人员在岗的场景提供高效、精准的实时监管服务，助力提升岗位管理效率与安全保障水平。

## 🎬 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

通过监控视频/现场图片对目标监测区域进行人员在岗状态分析，识别人员离岗、缺岗等异常状态，输出结构化的离岗监测分析报告

### 2. 🛠️ 能力范围

| 序号 | 具体能力 |
|---:|---|
| 1 | 人员存在检测 |
| 2 | 在岗定位识别 |
| 3 | 离岗状态判定 |
| 4 | 异常时长统计 |

### 3. ⚡ 触发条件

| 触发类型 | 触发规则 |
|---|---|
| ✅ 默认触发 | **默认触发**：当用户提供监控视频/图片 URL 或文件需要进行人员离岗监测时，默认触发本技能 |
| 🔎 明确分析意图 | 当用户明确需要进行人员离岗监测，提及人员离岗、缺岗监测、在岗检测、岗位监控等关键词，并且上传了视频或图片 |
| 📚 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、离岗监测报告清单、检测报告列表、查询历史报告、显示所有监测报告、离岗监测历史记录，查询离岗分析报告 |

### 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

_meta.json

{
  "ownerId": "kn7e2caqj7pnsvr9r7t8zenghs83xw7n",
  "slug": "smyx-staff-absence-detection-analysis",
  "version": "1.0.15",
  "publishedAt": 1791014765477
}

references/api_doc.md

# 人员离岗实时监测 API 接口文档

## 接口概览

人员离岗实时监测功能依赖云端 API 服务进行计算机视觉分析，以下是 API 接口规范说明。

## 认证方式

- API Key 通过请求头 `Authorization: Bearer {api_key}` 传递
- 未配置 API Key 时使用默认共享配额

## 主要接口

### 1. 离岗监测分析接口

**URL**: `{base_url}/api/v1/ai-analysis/personnel-leave-post-detection`

**Method**: `POST`

**请求参数**:

- `input_type`: `file` 或 `url`
- `media_type`: `video` 或 `image`
- `confidence_threshold`: 置信度阈值，默认 0.5
- `absence_threshold`: 离岗判定阈值（秒），默认 300
- `scene_code`: `PERSONNEL_LEAVE_POST_MONITORING`（固定值）
- `open_id`: 用户标识

**响应格式**: JSON

```json
{
  "code": 0,
  "message": "success",
  "data": {
    "id": "request-id",
    "detection_time": "2026-04-15 10:30:00",
    "detection": {
      "post_status": "leave_post",
      "abnormal_absence_count": 2,
      "total_absence_duration": 650,
      "status_stats": [
        {
          "status": "on_duty",
          "count": 1,
          "total_duration": 1200
        },
        {
          "status": "leave_post",
          "count": 2,
          "total_duration": 650
        },
        {
          "status": "temporary_leave",
          "count": 1,
          "total_duration": 80
        }
      ]
    }
  }
}
```

### 2. 查询历史报告列表接口

**URL**: `{base_url}/api/v1/ai-analysis/list`

**Method**: `GET`

**请求参数**:

- `scene_code`: `PERSONNEL_LEAVE_POST_MONITORING`
- `open_id`: 用户标识
- `start_time`: 起始时间（可选）
- `end_time`: 结束时间（可选）

### 3. 获取报告详情接口

**URL**: `{base_url}/api/v1/ai-analysis/detail/{request_id}`

**Method**: `GET`

## 错误码说明

| 错误码  | 说明      |
|------|---------|
| 0    | 成功      |
| 1001 | 参数错误    |
| 1002 | 文件格式不支持 |
| 1003 | 文件过大    |
| 2001 | 认证失败    |
| 2002 | 配额不足    |
| 3001 | 分析失败    |
| 4001 | 记录不存在   |

## 注意事项

1. 最大支持文件大小：10MB
2. 支持视频格式：mp4、avi、mov
3. 支持图片格式：jpg、png、jpeg
4. 分析时间根据文件大小不同，通常在 3-30 秒

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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