Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Automate construction photo report generation using n8n with AI-powered image analysis.
.claude/skills/datadrivenconstruction-n8n-photo-report/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | 50% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 97% | 0% |
Site photos require organization, analysis, and reporting. This workflow automates photo collection, AI analysis, and report generation.
[Photo Upload] → [AI Analysis] → [Categorization] → [Report Generation] → [Distribution]json{ "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/{{$today}}" } } ] }
json{ "name": "Analyze with Claude Vision", "type": "n8n-nodes-base.httpRequest", "parameters": { "method": "POST", "url": "https://api.anthropic.com/v1/messages", "headers": { "x-api-key": "={{$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": "={{$binary.data.toString('base64')}}" } }, { "type": "text", "text": "Analyze this construction site photo. Identify: 1) Work activity visible, 2) Approximate completion status, 3) Any safety concerns, 4) Weather conditions. Return JSON format." } ] }] } } }
json{ "nodes": [ { "name": "Parse AI Response", "type": "n8n-nodes-base.code", "parameters": { "jsCode": "const response = JSON.parse($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}}" } } } ] }
json{ "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 = $input.all();\n\nconst byCategory = {};\nlet safetyIssues = [];\n\nphotos.forEach(p => {\n const cat = p.json.fields.Category;\n if (!byCategory[cat]) byCategory[cat] = [];\n byCategory[cat].push(p.json.fields);\n \n if (p.json.fields['Safety Issues'] && p.json.fields['Safety Issues'] !== 'None') {\n safetyIssues.push({\n photo: p.json.fields.Filename,\n issue: p.json.fields['Safety Issues']\n });\n }\n});\n\nreturn [{\n json: {\n date: new Date().toISOString().split('T')[0],\n total_photos: photos.length,\n by_category: byCategory,\n safety_issues: safetyIssues,\n safety_count: safetyIssues.length\n }\n}];" } } ] }
json{ "name": "Send Report Email", "type": "n8n-nodes-base.emailSend", "parameters": { "toEmail": "={{$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}}" } }
pythonimport 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
ANTHROPIC_API_KEY for Claude Vision| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | fail→fail | 12,413 | 10,648 | -14% | 1 | 1 | 0% | 2,513 | 4,168 | +66% | 0 | 0 | — |
case-15 | fail→pass | 12,698 | 7,244 | -43% | 1 | 1 | 0% | 2,169 | 3,253 | +50% | 0 | 0 | — |
case-07 | pass→fail | 11,784 | 11,627 | -1% | 1 | 1 | 0% | 2,362 | 4,215 | +78% | 0 | 0 | — |
case-08 | fail→pass | 14,098 | 13,551 | -4% | 1 | 1 | 0% | 2,139 | 4,205 | +97% | 0 | 0 | — |
case-09 | fail→pass | 13,639 | 8,000 | -41% | 1 | 1 | 0% | 2,403 | 3,581 | +49% | 0 | 0 | — |
case-01 | fail→pass | 28,882 | 9,517 | -67% | 1 | 1 | 0% | 2,566 | 3,864 | +51% | 0 | 0 | — |
case-02 | fail→fail | 12,853 | 11,369 | -12% | 1 | 1 | 0% | 2,694 | 4,464 | +66% | 0 | 0 | — |
case-03 | fail→fail | 12,236 | 10,896 | -11% | 1 | 1 | 0% | 2,674 | 4,242 | +59% | 0 | 0 | — |
case-04 | pass→pass | 12,133 | 8,337 | -31% | 1 | 1 | 0% | 1,951 | 3,293 | +69% | 0 | 0 | — |
case-05 | fail→fail | 8,193 | 5,994 | -27% | 1 | 1 | 0% | 1,470 | 2,973 | +102% | 0 | 0 | — |
case-06 | fail→pass | 9,695 | 8,495 | -12% | 1 | 1 | 0% | 1,831 | 3,616 | +97% | 0 | 0 | — |
case-10 | fail→pass | 10,087 | 4,414 | -56% | 1 | 1 | 0% | 1,768 | 2,740 | +55% | 0 | 0 | — |
case-11 | fail→pass | 10,834 | 12,091 | +12% | 1 | 1 | 0% | 1,877 | 4,091 | +118% | 0 | 0 | — |
case-12 | fail→pass | 9,586 | 4,370 | -54% | 1 | 1 | 0% | 1,663 | 2,728 | +64% | 0 | 0 | — |
case-13 | pass→pass | 12,993 | 14,872 | +14% | 1 | 1 | 0% | 2,433 | 4,761 | +96% | 0 | 0 | — |
case-16 | fail→pass | 6,350 | 2,421 | -62% | 1 | 1 | 0% | 1,062 | 2,417 | +128% | 0 | 0 | — |
case-17 | fail→pass | 9,473 | 7,892 | -17% | 1 | 1 | 0% | 1,756 | 3,394 | +93% | 0 | 0 | — |
case-18 | pass→pass | 8,588 | 3,272 | -62% | 1 | 1 | 0% | 1,438 | 2,568 | +79% | 0 | 0 | — |
case-19 | fail→pass | 10,312 | 6,251 | -39% | 1 | 1 | 0% | 1,796 | 3,093 | +72% | 0 | 0 | — |
case-20 | pass→pass | 23,875 | 24,116 | +1% | 1 | 1 | 0% | 4,526 | 6,691 | +48% | 0 | 0 | — |
case-21 | pass→pass | 11,284 | 10,386 | -8% | 1 | 1 | 0% | 1,982 | 3,910 | +97% | 0 | 0 | — |
case-22 | pass→pass | 15,872 | 18,155 | +14% | 1 | 1 | 0% | 2,615 | 5,190 | +98% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +45 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.