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employee-off-duty-detection-2

作者 wsygcn · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install employee-off-duty-detection-2
功能描述
Intelligent workplace inspection system with guided setup, configurable inspection tasks, AI-powered image analysis, and Feishu alerting. Use when you need t...
使用说明 (SKILL.md)

Intelligent Inspection - 智能巡检

Overview

Intelligent Inspection is an intelligent workplace monitoring system that:

  • Captures images from surveillance cameras
  • Uses AI to analyze images based on user-defined inspection criteria
  • Sends alerts via Feishu (or other configured channels)
  • Supports guided configuration and isolated settings

This skill is designed for workplace safety, security monitoring, and employee presence verification.

Core Features

1. Guided Configuration

  • First-time setup wizard collects camera parameters
  • User-friendly prompts for API endpoints, tokens, and device settings
  • Configuration isolation in dedicated config file

2. Flexible Inspection Tasks

  • Customizable AI prompts for different inspection scenarios
  • Support for multiple camera devices and channels
  • Configurable patrol schedules and triggers

3. AI-Powered Analysis

  • Integration with OpenClaw's AI models
  • User-defined analysis criteria through custom prompts
  • Structured response handling for consistent results

4. Multi-Channel Alerting

  • Primary integration with Feishu messaging
  • Automatic fallback to other configured channels
  • Rich message formatting with images and metadata

Setup Requirements

Camera System Requirements

  • Supported camera APIs:
    • EZVIZ Cloud API (萤石云)
    • Generic RTSP/ONVIF cameras (via camsnap skill)
    • Custom HTTP-based capture endpoints

Authentication Requirements

  • Camera API access tokens or credentials
  • Feishu bot permissions (if using Feishu alerts)

OpenClaw Requirements

  • feishu channel configured (for Feishu alerts)
  • AI model with vision capabilities (e.g., qwen3-max, GPT-4 Vision)

Usage Scenarios

Employee Presence Monitoring

  • Detect if employees are at their workstations
  • Monitor break room usage
  • Track attendance during working hours

Security Patrols

  • Verify door/window status
  • Check for unauthorized access
  • Monitor restricted areas

Facility Inspections

  • Equipment status verification
  • Environmental condition monitoring
  • Compliance checks

Workflow

1. Initial Setup (First Run)

When the skill runs for the first time:

  1. Prompt user for camera system type
  2. Collect API endpoint, access token, device serial, channel number
  3. Ask for default inspection prompt template
  4. Confirm alert channel preferences
  5. Save configuration to ~/.openclaw/workspace/intelligent-inspection-config.json

2. Patrol Execution

For subsequent runs:

  1. Load configuration from config file
  2. Capture image using configured camera parameters
  3. Generate AI analysis prompt based on patrol task
  4. Execute AI analysis using OpenClaw's vision model
  5. Format and send alert via configured channels

3. Configuration Management

  • Configuration stored in isolated JSON file
  • Users can edit config file directly for advanced settings
  • Skill validates configuration on each run
  • Option to reset configuration and re-run setup

Configuration File Structure

The configuration file (intelligent-inspection-config.json) contains:

{
  "camera": {
    "type": "ezviz",
    "apiUrl": "https://open.ys7.com/api/open/cloud/v1/capture/save",
    "accessToken": "your-access-token",
    "deviceSerial": "C12345678",
    "channelNo": "1",
    "projectId": "intelligent-inspection-project"
  },
  "inspection": {
    "defaultPrompt": "请分析这张图片中是否有员工在工位上。如果没有人,请回复'离岗';如果有人,请回复'在岗'。",
    "alertOn": ["离岗"],
    "includeImage": true
  },
  "alerts": {
    "enabled": true,
    "channels": ["feishu"],
    "fallbackToDefault": true
  }
}

Error Handling

  • Invalid camera credentials: Clear error messages with setup guidance
  • AI analysis failures: Retry logic with fallback prompts
  • Alert delivery failures: Log errors and attempt alternative channels
  • Missing configuration: Automatically trigger setup wizard

Privacy and Security

  • Camera credentials stored locally only
  • Images processed through secure AI endpoints
  • No data retention beyond immediate patrol execution
  • Compliance with workplace monitoring regulations recommended

Integration Points

OpenClaw Tools Used

  • message: For sending alerts via configured channels
  • web_fetch/exec: For camera image capture
  • AI model calls: For image analysis

External Dependencies

  • Camera system APIs (EZVIZ, RTSP, etc.)
  • Feishu bot (if configured as alert channel)

Examples

Basic Employee Monitoring

执行智能巡检任务

Custom Inspection Task

执行智能巡检:检查会议室是否有人使用

Reset Configuration

重置智能巡检配置

Version History

  • v1.0.0: Initial release with EZVIZ support and Feishu alerts
安全使用建议
Before installing or running this skill: 1) Review and confirm where sensitive credentials will be stored — SKILL.md says ~/.openclaw/workspace/..., but the included index.js writes config.json in the skill directory. Decide which behavior you want and adjust the code or config accordingly. 2) Do not put production camera access tokens or Feishu bot secrets into the skill until you verify where they are stored and who/what can read them. Prefer short-lived credentials or per-skill scoped tokens if available. 3) Inspect any runtime hooks that perform web_fetch or exec in your OpenClaw runtime — these can issue arbitrary network requests or shell commands; ensure only trusted code runs them. 4) Confirm compliance with workplace privacy and local laws before enabling continuous monitoring. 5) If you can't audit how the OpenClaw platform will call external endpoints or handle images, treat the skill as higher-risk and avoid providing real credentials or production camera feeds until you can validate the end-to-end behavior.
功能分析
Type: OpenClaw Skill Name: employee-off-duty-detection-2 Version: 1.0.1 The skill implements a workplace surveillance system that requests sensitive camera API credentials and instructs the AI agent to use high-risk tools (exec, web_fetch) for image capture and alerting. While these capabilities are aligned with the stated purpose of 'Intelligent Inspection', the instructions in SKILL.md and the logic in index.js lack input sanitization or security constraints for shell execution, creating a potential command injection surface. Additionally, the metadata and configuration templates contain future-dated timestamps (2026) and placeholders that suggest the bundle may be a template or generated content rather than a fully vetted production tool.
能力评估
Purpose & Capability
The skill's name, description, and SKILL.md describe camera capture, AI image analysis, and Feishu alerts — all coherent. It relies on platform tools (message, web_fetch/exec, AI model) rather than embedding heavy dependencies, which is reasonable. Minor inconsistencies: the published skill slug/name is "employee-off-duty-detection-2" while SKILL.md and files use "intelligent-inspection"; SKILL.md says config lives in ~/.openclaw/workspace/intelligent-inspection-config.json but the included index.js writes/reads a local config.json in the skill directory. These mismatches could cause confusion about where sensitive credentials end up.
Instruction Scope
SKILL.md instructs the agent to capture camera images, generate AI analysis, and send alerts via platform channels using tools such as web_fetch and exec. That scope is expected for this functionality but also gives the skill the ability to call arbitrary HTTP endpoints and run shell commands — which can be abused to exfiltrate images or credentials if misused. The SKILL.md explicitly says it will save configuration to a home-path (~/.openclaw/...), but the shipped index.js actually saves to a local config.json — this contradiction is important because the documented path implies different access/retention semantics than the code implements.
Install Mechanism
There is no install spec (instruction-only behavior) and the included code is small and local. No downloads from remote URLs or package installs are requested. This is lower risk than an external installer, but presence of executable code (index.js) means you should review it before running.
Credentials
The skill legitimately needs camera API credentials and Feishu bot tokens for its task; SKILL.md describes storing them in a config file. However, the registry metadata lists no required env vars and no required config paths, which is inconsistent with runtime needs. The config/credential storage location is unclear (docs -> home path; code -> local skillDir). Storing long-lived camera or messaging tokens in an editable JSON file can be reasonable but requires the user to ensure it's stored securely and that retention behavior matches expectations.
Persistence & Privilege
The skill is not forced always-on and uses normal agent invocation. It does not request any platform-level privileges in the manifest. Autonomous invocation is allowed (the platform default) but does not by itself increase the assessed risk here.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install employee-off-duty-detection-2
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /employee-off-duty-detection-2 触发
  4. 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
v1.0.1
**Major update: Enhanced from basic off-duty detection to a flexible, intelligent inspection system.** - Introduced a guided, user-friendly setup wizard for camera and task configuration. - Added modular config file support, replacing in-code settings with isolated JSON config. - Expanded inspection beyond employee attendance: supports customizable prompts and multiple use cases (security, compliance, equipment). - Integrated robust AI-powered analysis and structured alerting, with multi-channel support (Feishu primary, with fallback). - Simplified file structure and improved maintainability; obsolete scripts removed and new entrypoint and config templates added.
v1.0.0
Employee off-duty detection skill initial release: - Monitors employee presence using real-time camera snapshots and custom AI analysis. - Integrates with Feishu to automatically send alerts when off-duty detected. - Supports manual execution; all required parameters are pre-configured. - Utilizes Ezviz AI platform for image analysis. - Easy setup—no additional parameters or configuration needed.
元数据
Slug employee-off-duty-detection-2
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

employee-off-duty-detection-2 是什么?

Intelligent workplace inspection system with guided setup, configurable inspection tasks, AI-powered image analysis, and Feishu alerting. Use when you need t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 303 次。

如何安装 employee-off-duty-detection-2?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install employee-off-duty-detection-2」即可一键安装,无需额外配置。

employee-off-duty-detection-2 是免费的吗?

是的,employee-off-duty-detection-2 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

employee-off-duty-detection-2 支持哪些平台?

employee-off-duty-detection-2 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 employee-off-duty-detection-2?

由 wsygcn(@wsygcn)开发并维护,当前版本 v1.0.1。

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