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n8n-photo-reportn8n 照片报告

Agent Skill

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

总安装

420

周安装

17

GitHub Stars

111

下载量

132
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:n8n-photo-report(n8n 照片报告)
来源仓库:https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
仓库路径:skills/n8n-photo-report
安装命令:
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill n8n-photo-report
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill n8n-photo-report

简介

用于辅助图像生成、图片编辑和视觉素材处理。n8n-photo-report 属于开发类 Skill,可作为该场景下的辅助能力补充。

  • 适合让 Agent 根据文本生成图片、处理背景或调用图像工具。
  • 使用时需确认输入图片、版权来源和输出格式限制。
  • 安装方式:通过 npx skills add 命令从指定 GitHub 仓库安装。
  • 注意:涉及人物、品牌或公开展示素材时应核对授权和真实性。

SKILL.md

n8n Photo Report Automation

Business Case

Site photos require organization, analysis, and reporting. This workflow automates photo collection, AI analysis, and report generation.

Workflow Overview

[Photo Upload] → [AI Analysis] → [Categorization] → [Report Generation] → [Distribution]

n8n Workflow Configuration

1. Photo Input Triggers

{
  "nodes": [
    {
      "name": "Photo Webhook",
      "type": "n8n-nodes-base.webhook",
      "parameters": {
        "httpMethod": "POST",
        "path": "photo-upload",
        "options": {
          "binaryData": true
        }
      }
    },
    {
      "name": "Watch Dropbox Folder",
      "type": "n8n-nodes-base.dropbox",
      "parameters": {
        "operation": "listFolder",
        "path": "/SitePhotos/{{$today}}"
      }
    }
  ]
}

2. AI Image Analysis

{
  "name": "Analyze with Claude Vision",
  "type": "n8n-nodes-base.httpRequest",
  "parameters": {
    "method": "POST",
    "url": "https://api.anthropic.com/v1/messages",
    "headers": {
      "x-api-key": "={{$env.ANTHROPIC_API_KEY}}",
      "anthropic-version": "2023-06-01"
    },
    "body": {
      "model": "claude-3-5-sonnet-20241022",
      "max_tokens": 1024,
      "messages": [{
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "base64",
              "media_type": "image/jpeg",
              "data": "={{$binary.data.toString('base64')}}"
            }
          },
          {
            "type": "text",
            "text": "Analyze this construction site photo. Identify: 1) Work activity visible, 2) Approximate completion status, 3) Any safety concerns, 4) Weather conditions. Return JSON format."
          }
        ]
      }]
    }
  }
}

3. Categorize and Store

{
  "nodes": [
    {
      "name": "Parse AI Response",
      "type": "n8n-nodes-base.code",
      "parameters": {
        "jsCode": "const response = JSON.parse($json.content[0].text);\n\nreturn [{\n  json: {\n    filename: $('Photo Webhook').first().json.filename,\n    timestamp: new Date().toISOString(),\n    activity: response.work_activity,\n    completion: response.completion_status,\n    safety_issues: response.safety_concerns,\n    weather: response.weather,\n    category: response.work_activity.includes('concrete') ? 'CONCRETE' :\n              response.work_activity.includes('steel') ? 'STEEL' :\n              response.work_activity.includes('mep') ? 'MEP' : 'GENERAL'\n  }\n}];"
      }
    },
    {
      "name": "Store in Airtable",
      "type": "n8n-nodes-base.airtable",
      "parameters": {
        "operation": "create",
        "table": "Site Photos",
        "fields": {
          "Filename": "={{$json.filename}}",
          "Date": "={{$json.timestamp}}",
          "Activity": "={{$json.activity}}",
          "Category": "={{$json.category}}",
          "Completion": "={{$json.completion}}",
          "Safety Issues": "={{$json.safety_issues}}"
        }
      }
    }
  ]
}

4. Generate Photo Report

{
  "nodes": [
    {
      "name": "Schedule Report",
      "type": "n8n-nodes-base.scheduleTrigger",
      "parameters": {
        "rule": {"interval": [{"field": "cronExpression", "expression": "0 18 * * 1-5"}]}
      }
    },
    {
      "name": "Get Today Photos",
      "type": "n8n-nodes-base.airtable",
      "parameters": {
        "operation": "list",
        "table": "Site Photos",
        "filterByFormula": "IS_SAME({Date}, TODAY(), 'day')"
      }
    },
    {
      "name": "Generate Report",
      "type": "n8n-nodes-base.code",
      "parameters": {
        "jsCode": "const photos = $input.all();\n\nconst byCategory = {};\nlet safetyIssues = [];\n\nphotos.forEach(p => {\n  const cat = p.json.fields.Category;\n  if (!byCategory[cat]) byCategory[cat] = [];\n  byCategory[cat].push(p.json.fields);\n  \n  if (p.json.fields['Safety Issues'] && p.json.fields['Safety Issues'] !== 'None') {\n    safetyIssues.push({\n      photo: p.json.fields.Filename,\n      issue: p.json.fields['Safety Issues']\n    });\n  }\n});\n\nreturn [{\n  json: {\n    date: new Date().toISOString().split('T')[0],\n    total_photos: photos.length,\n    by_category: byCategory,\n    safety_issues: safetyIssues,\n    safety_count: safetyIssues.length\n  }\n}];"
      }
    }
  ]
}

5. Distribution

{
  "name": "Send Report Email",
  "type": "n8n-nodes-base.emailSend",
  "parameters": {
    "toEmail": "={{$env.PHOTO_REPORT_RECIPIENTS}}",
    "subject": "Site Photo Report - {{$json.date}} ({{$json.total_photos}} photos)",
    "html": "<h2>Daily Photo Report</h2><p>Total Photos: {{$json.total_photos}}</p><h3>Safety Issues: {{$json.safety_count}}</h3>{{#if $json.safety_issues.length}}<ul>{{#each $json.safety_issues}}<li>{{photo}}: {{issue}}</li>{{/each}}</ul>{{/if}}"
  }
}

Python Helper

import requests
import base64

def upload_photo_to_workflow(image_path: str, webhook_url: str, metadata: dict):
    """Upload photo to n8n workflow."""
    with open(image_path, 'rb') as f:
        image_data = base64.b64encode(f.read()).decode()

    payload = {
        'filename': image_path.split('/')[-1],
        'image_data': image_data,
        'project_id': metadata.get('project_id'),
        'location': metadata.get('location'),
        'captured_by': metadata.get('captured_by')
    }

    response = requests.post(webhook_url, json=payload)
    return response.json()

def batch_upload_photos(photo_paths: list, webhook_url: str, project_id: str):
    """Batch upload multiple photos."""
    results = []
    for path in photo_paths:
        result = upload_photo_to_workflow(path, webhook_url, {'project_id': project_id})
        results.append(result)
    return results

Quick Start

  1. Import workflow to n8n
  2. Configure API keys:

- ANTHROPIC_API_KEY for Claude Vision - Airtable credentials - Email configuration

  1. Create Airtable base with "Site Photos" table
  2. Test with sample photo upload

Resources

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.33%
按下载量换算49

Claude

27.92%
按下载量换算37

Cursor

19.75%
按下载量换算26

Gemini CLI

9.53%
按下载量换算13

安全审计

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可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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