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N8n Photo Report

datadrivenconstruction/n8n-photo-report

Automate construction photo report generation using n8n with AI-powered image analysis.

2k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
264
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill n8n-photo-report

The instruction itself

12 sections, as written by the author

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

  • Import workflow to n8n
  • Configure API keys:
  • ANTHROPIC_API_KEY for Claude Vision
  • Airtable credentials
  • Email configuration
  • Create Airtable base with "Site Photos" table
  • Test with sample photo upload

Resources

  • n8n Documentation: https://docs.n8n.io
  • Claude Vision API: https://docs.anthropic.com
  • DDC Book: Chapter 4.2 - Workflow Automation

How to use it

Copy the folder

Take datadrivenconstruction/n8n-photo-report from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.